CN108648213A - A kind of implementation method of KCF track algorithms on TMS320C6657 - Google Patents

Abstract

The invention belongs to target following technical field, a kind of implementation method of KCF track algorithms on TMS320C6657 is disclosed, based on the hardware platform of TMS320C6657 processors, to two-dimensional fast fourier transform FFT2D algorithm optimizations；Language level code optimization, including loop unrolling are carried out to KCF algorithms, using Inline Function, optimize program structure.The result of actual measurement shows that the image of 320 × 240 pixels, when tracking target is 64 × 32 pixel, wave door size is 128 × 64.The tracking time of each frame is about 15310000 cpu cycles, if DSP is operated under the frequency of 1GHz, taking for each frame is no more than 15.5 milliseconds, and about each second handles 64 frame images, meets requirement of real-time.

Description

A kind of implementation method of KCF track algorithms on TMS320C6657

Technical field

The invention belongs to a kind of reality of target following technical field more particularly to KCF track algorithms on TMS320C6657 Existing method.

Background technology

Currently, the prior art commonly used in the trade is such：Computer vision is one and lives in frontier of knowledge, very The research field of jump, it is different from the research to mankind or animal vision in biology, but is known by means of image procossing, pattern Not, the knowledge such as mathematics and physics come establish model and processing data.To allow computer to have intelligent understanding to extraneous object, It can assist it that object with the real world is identified, be like that computer has " vision ", widen the view and see the world. Computer vision replaces human eye to be observed and detected using machine, can not only improve precision and detection efficiency, Er Qieke To be difficult to reach applied to some human eyes or human eye is difficult to the occasion met the requirements.The recognition and tracking of target is always to calculate One research hotspot of machine visual field.Identification refers mainly to computer according to certain feature and experience accumulation, for receiving Certain information (be typically image), analyze content therein, its attribute differentiated；And it is typically on having sequential to track On the basis of hereafter, continuous tracing and positioning is carried out for the target in computer " visual field ".In traditional target tracking domain In, it usually needs the image of target is pre-saved as template.Into when line trace, chosen using some search strategies in the visual field It may be the region undetermined of target, compare the similarity of template and region to be searched according to mathematics or physical method later, finally The size of similarity is tracked to determine the position of target, completes tracking.Typical search strategy has global search, slightly matches refinement Matching etc., common similarity calculating method include Euclidean distance, histogram COS distance, score distance, cut that distance, this is more by person of outstanding talent Husband's distance, histograms of oriented gradients, fuzzy characteristics comparison etc., further include color histogram etc. for colored image.But The feature that traditional tracking uses is all relatively simple, and tracking result is barely satisfactory, and needs the mould that target is obtained ahead of time Plate image, is difficult to realize in some cases.On this basis, numerous scholars have investigated the tracking of on-line study formula, This kind of method can be directed to arbitrary target and carry out real-time tracking.Operator only needs the initial end in tracking sequence artificial Specified target (it includes mesh target area that one is generally referred on PC), track algorithm will be according to selected target It practises, the model obtained according to study is into line trace in new video frame, and the result that algorithm obtains tracking is as sample, into one Step is learnt, and is updated to model, is so moved in circles, is continuously tracked to video sequence.Because this one kind is calculated Method can adaptively learning objective model, therefore the various interference for occurring in sequence have stronger adaptability, KCF Algorithm is exactly a wherein very classical track algorithm.KCF track algorithms by learn regularization least square grader come Differentiate target, and introduce circular matrix and cyclic shift is carried out to training sample, can approximation regard as, pass through that learn regularization minimum Two multiply grader to differentiate target, and introduce circular matrix to training sample carry out cyclic shift, can approximation regard as to target Intensive sampling then utilize circular matrix by correlation filter and kernel function to efficiently obtaining a large amount of training sample It connects, to propose core correlation filter (KCF).In recent years, with high speed digital signal processor (digital Signal processer, DSP) development, have many high performance DSP that can quickly carry out the mathematical operation of large amount of complex, Larger memory space can also be improved simultaneously, meets the real-time demand in terms of data processing, with the TMS320 series of TI companies For, the chips such as C6416, C665x, C667x in C6000 series are obtained all in real-time target following and are widely answered With.But most of track algorithm in such platform is all the fairly simple traditional algorithm of such as MAD, NCC, tracking effect Fruit is barely satisfactory.Since data volume is big in object tracking process for the new algorithms such as KCF, data correlation is high, hinders it in reality Application in the engineering of border.Therefore how KCF algorithms to be optimized using the characteristics and advantages of DSP platform, becomes realization and calculates The key of method tracking system.TMS320C6657 is that the having based on KeyStone frameworks of TI companies publication pinpoints and floating-point meter The double-nuclear DSP of calculation ability.Chip interior integrates two C66x series kernels, and each core highest may operate at 1.25GHz clock frequencies Rate, monokaryon performance are four times of TMS320C64x+ series DSP processing capacities, and monoblock chip possesses 80GMACS or 40GFLOPS fortune Calculation ability.C66x kernels are integrated with 90 completely new instructions for floating-point and towards vector math calculation process, are very suitable for counting The functions such as word signal processing and figure acquisition.In addition the C6000 fixed points before C66x kernels and TI and Floating-point DSP kernel are to offspring Code is compatible, it is ensured that software portability shortens software development cycle.TMS320C6657 multi-core DSPs are integrated with abundant on piece Storage resource, single kernel possess the level-one program cache area of 32KB sizes and the level one data buffer area of 32KB sizes, and It can be configured to the special region of memory of the 1024KB sizes of RAM or caching.Meanwhile two kernels share the interior of 1024KB sizes Deposit space.Further include 32 DDR3 that can be operated in 1333MHz frequencies in addition, in order to quickly access chip external memory External memory interface.For different application scenarios, a large amount of Peripheral Interface resources of integrated chip：The four roads channels SRIO, it is single Channel highest transmission speed is up to 5Gbps；One gigabit Ethernet mouth；Two generation PCIe interfaces；HyperLink high-speed interfaces；It is logical With parallel serial ports (uPP) etc..KCF algorithm introductions：Among the research in vision tracking field in recent years, one of maximum breakthrough It is exactly widely to introduce the method learnt based on discrimination model.The problem of vision tracks can naturally be converted into one The problem of a on-line study.A given initial pictures block for including tracking target, the purpose of tracking is then to train one A identification and classification device distinguishes tracking target and background.To in the training process of identification and classification device, it is easy to will pay close attention to Point is placed on interested tracking target --- for the positive sample of classifier training.But the core principles of method of discrimination are then Same or more focus are placed on the background around tracking target --- for the negative sample of classifier training.Most often The method of acquisition negative sample is that the different location around tracking target chooses a certain number of image blocks, then is carried out to it Scaling in varying degrees and transformation.During acquiring negative sample, a very challenging factor is then It is --- the negative sample that can be obtained from an image is almost inexhaustible.But since vision is tracked to real-time Requirement, present Vision Tracking can only find between negative sample quantity as much as possible and relatively low operand One equalization point.Common practice is a certain number of negative samples of selection random in each frame for classifier training, without It is to take principle exhausted.KCF track algorithms are broadly divided into detection, training and update three phases.During sample training, A large amount of training sample is obtained by carrying out cyclic shift to selected target, classification is then trained by the sample of acquisition Device.During target detection, the related coefficient between area to be tested and actual tracking target, choosing are calculated using kernel function Take related coefficient is maximum to wait for favored area as new target.Meanwhile using two-dimensional fast fourier transform (FFT2D) by time domain It is mapped to frequency domain, reduces the operation in training and detection process.Finally when fast target detects, quick Fu of two dimension is utilized again In leaf inverse transformation (IFFT2D) frequency domain is mapped back into time domain, obtain the position that target in present frame occurs.The algorithm is revolved in target Turn, partial occlusion etc. has good tracking effect.Currently, all concentrating on anti-serious screening for most of research of KCF Gear, anti-target loss, anti-dimensional variation etc., it is intended that stable long time-tracking method is developed into, still, for KCF Application of the method in Practical Project is but seldom related to.Although the KCF algorithm speeds of service are very fast, it is needed during tracking Carry out a large amount of FFT2D operations, for realize platform computing capability have very high requirement, if platform computing capability compared with Difference can then extend the time of tracking consumption, lose the advantage of KCF track algorithms；During tracking simultaneously, need largely to assist Matrix is also larger for the demand of memory for calculating.In conclusion problem of the existing technology is：Although KCF is flat in PC The effect of platform is fine, but is applied in Practical Project not yet.A large amount of floating point arithmetic involved in KCF algorithms, The calculating speed of platform is required very high；The operation involved in algorithm is mostly based on complex field simultaneously, therefore for memory Consumption be significantly larger than traditional algorithm.The different module such as tracking, detection, study all respectively needs some complementary squares in algorithm Gust, what is preserved in these matrixes is all complex number type floating number, and committed memory is 8 times of same size gray level image, is accounted for memory With quite greatly, the memory of embedded platform is generally smaller, it is difficult to which directly transplanting is realized.And in KCF algorithms include a large amount of Exponent arithmetic, matrix operation, FFT operations, calculate considerably complicated, for the more demanding of platform, realize that difficulty is bigger.

In conclusion problem of the existing technology is：KCF algorithms are applied in Practical Project not yet, and Its calculating is considerably complicated, for the more demanding of platform, realizes that difficulty is bigger.

Solve the difficulty and meaning of above-mentioned technical problem：KCF algorithms are during tracking, it is necessary first to selected target, so Needing the size according to institute's selected target to choose pending image block afterwards, (usual image block size is the 2.5 of selected target Times), the size for calculating required companion matrix in the algorithm is all related to the size of image block.It is largely transported in KCF algorithms At last based on floating number, but the operation of floating number or the operation of floating number are not supported in some Embedded platforms Efficiency will be far below the operation efficiency of fixed-point number.The also most important, a large amount of operations carried out in the algorithm are all bases In complex field, therefore the data stored in matrix need to include real part and imaginary part, are typically embedded into formula platform and utilize 8-bit type numbers According to image is preserved, such as char types, but if preserving complex number type floating number, the memory consumed is 8 times of original image size, The size of image can be handled by greatly limiting.Meanwhile in calculating process including a large amount of exponent arithmetic, matrix operation, FFT Operation, if needed to calculate the Gaussian kernel correlation between characteristics of image and trace model when tracking, detection, when more new model, needs There are dot product, the point division operation of matrix, it is extremely complex and time-consuming, for embedded platform, a large amount of such operation meeting pair CPU causes prodigious load.If do not optimized to algorithm, the consumption of memory conference cause tracking during memory overflow Go out, tracking is gently then caused to fail, it is heavy then can cause program crashing or even chip is burnt.

For KCF as one of the algorithm that public attention is most contained in the world in recent years, the performance in terms of tracking is very good. But it is limited to the limitation of PC platforms, it can not be used in many occasions, such as the terminal guidance from motion tracking, guided missile of unmanned plane Etc..In fact, these application scenarios are very big for the demand of outstanding track algorithm, but this kind of application scenarios In, track algorithm is conventionally based on embedded platform.If can solve the problems, such as that KCF algorithms are transplanted in embedded platform, just KCF algorithms can be applied in a large amount of simple device, such as intelligent video camera head, unmanned plane, even military aspect.At these , can be on the basis of target following in application scenarios, the function of Improving Equipment improves its applicability, convenient in target following On the basis of further extended function.

Invention content

In view of the problems of the existing technology, the reality the present invention provides a kind of KCF track algorithms on TMS320C6657 Existing method.

The invention is realized in this way a kind of implementation method of KCF track algorithms on TMS320C6657, the KCF with Implementation method of the track algorithm on TMS320C6657 include：Based on the hardware platform of TMS320C6657 processors, realize that KCF is calculated Method.In order to ensure the speed of service of algorithm, without using piece external storage, all data are distributed and are all stored in piece in calculating process Middle completion, in particular it is necessary to which the picture frame of processing is stored in internuclear shared drive, such selection can both save CPU Core enjoys memory certainly, while also allowing for the cooperation operation of subsequent development multinuclear, and image data can directly be read in shared drive, Avoid address conversion operation.According to KCF algorithm flows, training, detection-phase are required for calculating the Gauss nuclear phase between characteristics of image Guan Xing realizes the quick meter to Gaussian kernel correlation so first according to kernel function and the property of circular matrix in DSP platform It calculates.On this basis, the modules such as training and the detection in KCF algorithms are completed, while also having the basic operations largely used --- FFT2D operations.Algorithm operates on one of TMS320C6657 CPU cores, when handling new frame image, is read in MSM Required image data is taken, so the memory size that algorithm can use is 1MB.Due to needing to consume prodigious memory in algorithm As auxiliary space when calculating, therefore it is susceptible to memory overflow problem, on this basis, needs further to optimize, Ensure feasibility and real-time, including to two-dimensional fast fourier transform FFT2D algorithm optimizations；Language level is carried out to KCF algorithms Code optimization, including loop unrolling, use Inline Function；Optimize program structure, optimizes the distribution etc. of memory space.

Further, implementation method of the KCF track algorithms on TMS320C6657 optimizes FFT2D, for defeated The image to be calculated entered carries out FFT transform to each row, then carries out FFT transform to each row, by being obtained after converting twice Be exactly FFT2D result.

Further, memory space is smaller, needs auxiliary vector t, and data line is copied in vectorial t, calculates FFT knots Fruit is written original position, recycles this process and terminate until all rows all calculate, and completes row transformation；The data of each row are answered successively It makes in vectorial t, calculates FFT result, be as a result written in original matrix, recycle the operation until completing all row, obtain being FFT2D Result.

Further, implementation method of the KCF track algorithms on TMS320C6657 calculates floating-point using cossp functions Several cosine value, for calculating Hamming window；Gaussian matrix is calculated using sqrtsp, divsp and expsp；Use DSPF_sp_ Maxidx, DSPF_sp_maxval come the maximum value in quick obtaining matrix and subscript.

Further, image to be tracked is stored in by implementation method of the KCF track algorithms on TMS320C6657 In the MSM of DSP, auxiliary data needed for track algorithm is distributed in enjoying certainly for CPU core in memory.

Further, the Compiler Optimization of implementation method of the KCF track algorithms on TMS320C6657 is provided by TI Programming tool CCS complete.

Further, implementation method of the KCF track algorithms on TMS320C6657 replaces integer using shifting function Multiplication and division operation；Cycle is optimized, larger interior cycle is used.

In conclusion advantages of the present invention and good effect are：The present invention proposes a kind of reality by DSP embedded platforms When Target Tracking System implementation, based on the hardware platform of TMS320C6657 processors, to two-dimensional fast fourier transform (FFT2D) algorithm optimization is carried out, language level code optimization, including loop unrolling secondly are carried out to KCF algorithms, use inline letter Number, optimization program structure etc., the exquisite place of KCF algorithms is, is used in each frame to identification and classification device thousands of negative Sample is trained, to ensure precision when tracking.At the same time, which is become using circular matrix by discrete fourier again The characteristic under frequency domain after (DFT) is changed, avoids and directly traverses each negative sample, to ensure that algorithm has high reality Shi Xing.The result of actual measurement shows that the image of 320 × 240 pixels, when tracking target is 64 × 32 pixel, wave door size is 128 × 64.The tracking time of each frame is about 15310000 cpu cycles, if DSP is operated under the frequency of 1GHz, each frame Take be no more than 15.5 milliseconds, about each second handle 64 frame images, meet requirement of real-time.

Description of the drawings

Fig. 1 is implementation method flow chart of the KCF track algorithms provided in an embodiment of the present invention on TMS320C6657.

Fig. 2 is the target location schematic diagram provided in an embodiment of the present invention exported according to algorithm.

Fig. 3 is the effect application of implementation method of the KCF track algorithms provided in an embodiment of the present invention on TMS320C6657 Schematic diagram.

Specific implementation mode

In order to make the purpose , technical scheme and advantage of the present invention be clearer, with reference to embodiments, to the present invention It is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, it is not used to Limit the present invention.

Artificial intelligence, image procossing, pattern-recognition, target detection, machine learning etc. have been merged in the target following of the present invention Advanced technology in multiple fields.It obtains mesh by detection, extraction, identification to target object in image sequence and study The relevant informations such as target shape, position, speed, track, then by the analysis and processing to relevant information, realize and target is moved The understanding of behavior is finally completed the tasks such as tracking, prediction.

As shown in Figure 1, implementation method of the KCF track algorithms provided in an embodiment of the present invention on TMS320C6657 includes Following steps：

S101：Based on the hardware platform of TMS320C6657 processors, two-dimensional fast fourier transform (FFT2D) is carried out Algorithm optimization；

S102：Language level code optimization, including loop unrolling are carried out to KCF algorithms, using Inline Function, optimize program knot Structure.

The application principle of the present invention is further described below in conjunction with the accompanying drawings.

1, the realization and optimization of DSP platform：

(1) there is higher effect than DFT2D using FFT2D come to replace DFT2D, FFT2D be two-dimensional Fast Fourier Transform (FFT) Rate.It is not only complicated but if FFT2D operations are realized in pure hand weaving, but also take huge.FFT2D operations and IFFT2D operations exist It is repeatedly called in the present invention, is the bottleneck place of real-time, if using hand-code, it is impossible to reach requirement of real-time.It is first First FFT2D is optimized.DSP platform has very good embedded support for mathematic(al) manipulations such as FFT, but only one-dimensional FFT function APIs for using, according to the property of FFT2D, two-dimensional FFT result can be calculated using one-dimensional FFT.For defeated The image to be calculated entered first carries out FFT transform to each row, and referred to as row variation carries out FFT transform to each row again later, Referred to as rank transformation, by convert twice obtain later be exactly FFT2D result.FFT operations for each row or column, can It is calculated with calling the DSPF_sp_fftSPxSP functions in dsplib, this is the library function that TI officials provide, for FFT operations The optimization of hardware view has been carried out, the operational capability of DSP is made full use of, computational efficiency can be greatly speeded up.Similarly, IFFT2D Same mode may be used to be calculated.In this calculating process, row transformation and the sequencing of rank transformation do not influence most Whole result.If memory space is smaller, an auxiliary vector t is only needed.Data line is copied into vectorial t successively first In, FFT result is calculated, original position is written, recycles this process and terminates until all rows all calculate, completes row transformation at this time.Then The data of each row are copied in vectorial t successively, FFT result is calculated, writes the result into original matrix, cycle is until completing institute The operation for having row, what is obtained at this time is exactly the result of FFT2D；

(2) use Inline Function, Inline Function that can be mapped directly into the compilation of C6000, efficiency of code execution and remittance It compiles quite, far above common pure hand weaving code.TI companies provide abundant auxiliary library function, mathlib, dsplib, Imglib etc., which provide a large amount of common Inline Function API, for realizing the operation of fixed point and floating number.Occur in program A large amount of matrix dot product, point division operation and the vectorial modulus value of calculating etc. take longer operation, can be used in C6657 inline Function optimizes.Cossp functions are such as used to calculate the cosine value of floating number, for calculating Hamming window；Using sqrtsp, Divsp and expsp etc. calculates Gaussian matrix；Carry out quick obtaining matrix using DSPF_sp_maxidx, DSPF_sp_maxval In maximum value and subscript etc..Such as the calculating of Gaussian function, if using simple standard c functions execute once-through operation when Between about 7000us, reasonable employment Inline Function can be reduced to 2000us, and efficiency greatly promotes.

(3) data store optimization, when PC platforms run track algorithm, the distribution and recycling of memory are all by operating system Agency, but the memory in DSP needs developer to distribute, the problems such as otherwise spilling it is easy to appear memory.It is tracked in KCF In algorithm, calculating, detection process, training process of FFT2D etc. are required for using companion matrix, if being not added with optimization, DSP Memory use far from enough, can then reduce computational efficiency using piece external storage.By the compression to companion matrix, the piece of C6657 Upper resource has met the EMS memory occupation of program.In the implementation of this paper, image to be tracked is stored in the MSM of DSP In (Multicoreshared memory, internuclear shared drive), the auxiliary data needed for track algorithm is interior in enjoying certainly for CPU core Deposit middle distribution.That is, the double-core of C6657 can respectively run a trace routine, can track in piece image simultaneously Two targets, can further develop multiple target tracking.

(4) Compiler Optimization, the programming tool CCS provided by TI provide powerful compiling optimization come what is completed in CCS It instructs to complete the function of optimization, by reasonably selecting compiling option, the operational efficiency of code can be promoted.Compiler it is excellent It is as shown in table 1 to change option：

1 Compiler Optimization option of table

(5) some other optimizations of program level, such as replace the multiplication and division operation of integer using shifting function；To recycle into Row optimization, improves the execution efficiency of code using larger interior cycle as possible；Reasonable employment keyword and global variable are reduced The code of redundancy improves execution efficiency.

The application effect of the present invention is explained in detail with reference to experiment.

1, experimental result is demonstrated：

Under CCS environment, makes to show a C language KCF track algorithms, and optimize, be transplanted to TMS320C6657 Development board, to resolution ratio be 320 × 240 image sequence into line trace, target sizes are 64 × 32, and wave door size is 2 times of mesh Mark size.

Track algorithm only operates in monokaryon, and DSP reads in video sequence in MSM successively, after reading, CPU operation with Track algorithm exports the position the result is that target.In first frame, algorithm will extract feature according to the initial position of target, carry out Training, will subsequently be continuously tracked target.The tracking situation of object observing and processing time confirm the feasibility of system And time performance.It according to the target location that algorithm exports, is marked in artwork, it is observed that just whether the result of tracking Really.As shown in Figure 2.Four width figures are the tracking situations of different frame in video sequence, it can be seen that in each frame, algorithm is obtained Position and observation obtain the result is that coincideing, even if in 164 frames, billboard is slight to vehicle to be blocked, but still It accurately tracks.It can thus be seen that algorithm is successful for the tracking of target, as shown in Figure 3.

In the 165 to 173rd frame, black vehicle is seriously blocked by billboard, it can be seen that in this process, algorithm is It realizes and accurately tracks, and after black reappears, still tenacious tracking.This is that traditional NCC algorithms cannot achieve 's.

It in summary it can be seen, the algorithm keeps track effect that the present invention realizes is stable, and compared with traditional NCC algorithms, Situations such as blocking, is more robust.Before carrying out storage optimization, there is memory spilling at runtime in DSP6657, leads to program It interrupts, can not continue to track.After carrying out storage optimization, when not carrying out FFT2D optimizations and compiling optimization, the run time of each frame About 243 milliseconds, the speed of service is very slow.After the method proposed in the present invention optimizes, when the operation of each frame Between about 15.5 milliseconds, efficiency improves about 16 times, has reached real-time requirement.

The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention All any modification, equivalent and improvement etc., should all be included in the protection scope of the present invention made by within refreshing and principle.

Claims (7)

1. a kind of implementation method of KCF track algorithms on TMS320C6657, which is characterized in that the KCF track algorithms exist Implementation method on TMS320C6657 includes：Based on the hardware platform of TMS320C6657 processors, to two-dimentional fast Fourier Convert FFT2D algorithm optimizations；Language level code optimization, including loop unrolling are carried out to KCF algorithms, use Inline Function, optimization Program structure.

2. implementation method of the KCF track algorithms as described in claim 1 on TMS320C6657, which is characterized in that described Implementation method of the KCF track algorithms on TMS320C6657 optimizes FFT2D, for the image to be calculated of input, to every A line carry out FFT transform, then to each row carry out FFT transform, by convert twice obtain later be exactly FFT2D result.

3. implementation method of the KCF track algorithms as claimed in claim 2 on TMS320C6657, which is characterized in that storage is empty Between it is smaller, need auxiliary vector t, data line copied in vectorial t, calculate FFT result, be written original position, recycle this mistake Journey terminates until all rows all calculate, and completes row transformation；The data of each row are copied in vectorial t successively, calculate FFT knots As a result fruit is written in original matrix, recycle the operation until completing all row, obtain be FFT2D result.

4. implementation method of the KCF track algorithms as described in claim 1 on TMS320C6657, which is characterized in that described Implementation method of the KCF track algorithms on TMS320C6657 calculates the cosine value of floating number using cossp functions, for calculating Hamming window；Gaussian matrix is calculated using sqrtsp, divsp and expsp；Use DSPF_sp_maxidx, DSPF_sp_maxval Come the maximum value in quick obtaining matrix and subscript.

5. implementation method of the KCF track algorithms as described in claim 1 on TMS320C6657, which is characterized in that described Image to be tracked is stored in the MSM of DSP by implementation method of the KCF track algorithms on TMS320C6657, track algorithm institute The auxiliary data needed is distributed in enjoying certainly for CPU core in memory.

6. implementation method of the KCF track algorithms as described in claim 1 on TMS320C6657, which is characterized in that described The Compiler Optimization of implementation method of the KCF track algorithms on TMS320C6657 is completed by the programming tool CCS that TI is provided.

7. implementation method of the KCF track algorithms as described in claim 1 on TMS320C6657, which is characterized in that described Implementation method of the KCF track algorithms on TMS320C6657 replaces the multiplication and division operation of integer using shifting function；To recycle into Row optimization, uses larger interior cycle.