CN111161325A - Three-dimensional multi-target tracking method based on Kalman filtering and LSTM - Google Patents
Three-dimensional multi-target tracking method based on Kalman filtering and LSTM Download PDFInfo
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Abstract
The invention discloses a three-dimensional target tracking method based on Kalman filtering and LSTM, which comprises the following steps: initializing a track of an input three-dimensional target frame; updating and denoising the three-dimensional target frame track by using a constant velocity Kalman filtering algorithm to obtain a prediction track set; performing data association on the predicted track and the three-dimensional target frame of the current frame by using a Hungarian algorithm and updating a Kalman filter; the denoised three-dimensional target frame sequence is used for training a long-time memory network; and tracking and predicting the three-dimensional target by using a constant velocity Kalman filtering algorithm and a Hungarian algorithm and the trained LSTM. The traditional target tracking method based on Kalman filtering has the problem of insufficient nonlinear fitting capability, and the method is different from the traditional method in that the powerful feature extraction capability of a deep learning model LSTM is used, so that a more complex motion model can be fitted, the tracking result is smoother, and the speed of a tracking system is improved.
Description
Technical Field
The invention relates to the field of computer vision, in particular to a three-dimensional multi-target tracking method based on Kalman filtering and LSTM.
Background
Three-dimensional multi-target tracking is an important component of video processing and computer vision, is widely applied to automatic driving, benefits from improvement of accuracy of detection algorithms, and is mainly based on detection tracking in the current tracking technology. In a tracking algorithm based on detection, a target detector detects images of each frame to obtain a target detection frame, and then association and tracking of the target frame are carried out by utilizing motion information and frame information of the target to obtain a track of the target.
When the traditional tracking algorithm based on detection is applied to three-dimensional multi-target tracking, the real-time performance of the tracking algorithm is seriously dependent on the detection speed of a three-dimensional target detector. Because the current mainstream three-dimensional target detector has low speed, the existing tracking algorithm based on detection cannot be directly applied to three-dimensional multi-target tracking. Meanwhile, the three-dimensional frame of the tracked target shakes obviously due to the noise existing in the three-dimensional frame of the target output by the detector, so that the tracking result is not smooth and stable enough.
Disclosure of Invention
In order to solve the above technical problem, an embodiment of the present invention provides a three-dimensional multi-target tracking method based on kalman filtering and LSTM, including:
s1, track initialization is carried out on the input three-dimensional target frame, initialization is carried out, whether a track is newly established or not is determined according to whether the three-dimensional frame of the t +1 th frame is matched with the three-dimensional frame of the t th frame, and due to the fact that false positive samples possibly exist in a three-dimensional target detection result, the new track can be initialized only when the same target appears in two continuous frames;
s2, updating and denoising the t-th frame three-dimensional target frame track by using a constant velocity Kalman filtering algorithm and obtaining a real track set
Then, predicting to obtain a predicted track set
Wherein the predicted trajectory set
A set of predicted trajectories representing a t +1 th frame;
s3, performing data association on the predicted track and the three-dimensional target frame of the current frame by using the Hungarian algorithm and updating a Kalman filter;
s4, using the denoised three-dimensional target frame sequence for training a long-time memory network LSTM;
s5, tracking and predicting a three-dimensional target by using a constant-rate Kalman filtering algorithm and a Hungarian algorithm and a trained LSTM, if each frame is subjected to three-dimensional target frame detection, because a mainstream three-dimensional target detector generally has the problem of low detection speed, a three-dimensional target detection result can be obtained every F frames, and the middle F frame is predicted by using an LSTM model, so that the tracking result is smoother and the speed is increased.
Further, the track initialization process of step S1 is as follows:
by using
The ith three-dimensional object bounding box representing the tth frame,
the three-dimensional target frame comprises a camera coordinate system, a three-dimensional target frame, a camera coordinate system and a camera coordinate system, wherein x, y, z, l, w, h and theta respectively represent coordinates of the three-dimensional target frame on an x axis, a y axis and a z axis of the camera coordinate system;
the length, width and height of the three-dimensional target frame and the observation angle of the target, set DtA set of all three-dimensional target bounding boxes representing a t-th frame;
if the intersection ratio is higher than the mean
That is, when the Intersection ratio (IoU for short) of the ith three-dimensional target frame in the t frame and the jth three-dimensional target frame in the t +1 frame is greater than or equal to the threshold value threshold, a new track is created
Where k represents the kth track, and the set of tracks at time T +1 is denoted as Tt+1And discarding the remaining three-dimensional target frame.
Further, the data association of step S3 is specifically as follows:
collecting the three-dimensional target frame D of the current t frametPrediction track set T obtained by Kalman filtering algorithmt pThe Hungarian algorithm is input to obtain a data correlation result;
in the result, three-dimensional target frame set DtDivided into two sets
Respectively representing a matched three-dimensional detection frame set and an unmatched three-dimensional target frame set by using
Update, for collections
Executing step S1 to initialize the three-dimensional target track;
in the result of data association, the unmatched traces will be discarded and the matched traces will be retained.
Further, the training long-term memory network LSTM in step S4 is specifically as follows:
and setting a time step L of the LSTM, cutting the tracks in the track set according to the frame number L +1, discarding the tracks with the insufficient length L +1, taking the three-dimensional target frame sequence of the previous L frames as the input of the LSTM, taking the last frame as the label of the LSTM, and training the LSTM to obtain the three-dimensional target track prediction model.
Further, the tracking and predicting of the three-dimensional target of step S5 are specifically as follows:
setting an interval frame number F, if the current track frame number is not equal to N (F +1) +1 and is greater than L, starting an LSTM network to predict a three-dimensional target frame of the next frame, wherein N is a natural number, acquiring the three-dimensional target frame every other F frames, performing target tracking on a three-dimensional target frame sequence of the interval F frames by adopting a constant rate Kalman filtering algorithm and a Hungarian algorithm, namely executing the steps S1 to S3 to obtain a de-noised three-dimensional target frame, and merging the prediction results of the two models into a final tracking result.
Compared with the prior art, the invention has the following advantages and effects:
high efficiency: according to the method, the LSTM is used for predicting the three-dimensional target frame with the intermediate interval frame number, so that the times of acquiring the three-dimensional target frame by a tracking algorithm are reduced, and the speed of the three-dimensional multi-target tracking method is greatly improved;
stability: due to the strong nonlinear fitting capability of the depth network LSTM, the track jitter output by the tracking algorithm is smaller and more stable.
Drawings
FIG. 1 is a diagram of Kalman filtering and LSTM fusion for processing different frames according to a first embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
First embodiment
The embodiment discloses a layer-by-layer updating algorithm based on generation of a countermeasure network, which specifically comprises the following steps:
and step S1, track initialization is carried out on the input three-dimensional target frame, initialization is carried out to determine whether a track is newly established according to whether the three-dimensional frame of the t +1 th frame is matched with the three-dimensional frame of the t th frame, and because some false positive samples may exist in the three-dimensional target detection result, the new track is initialized only when the same target appears in two continuous frames. The specific process is as follows:
by using
The ith three-dimensional object bounding box representing the tth frame,
wherein x, y, z, l, w, h and theta respectively represent the coordinate of the three-dimensional target frame on the x axis, the y axis coordinate and the z axis coordinate of the camera coordinate system, the length, the width and the height of the three-dimensional target frame and the observation angle of the target, and the set DtSet of all three-dimensional object bounding boxes representing the t-th frame, if cross-over
If the threshold is 0.7, that is, the intersection ratio IoU between the ith three-dimensional target frame in the t-th frame and the jth three-dimensional target frame in the t + 1-th frame is greater than or equal to the threshold, then a new track is created
Where k represents the kth track, and the set of tracks at time T +1 is denoted as Tt+1And discarding the remaining three-dimensional target frame.
Step S2, updating and denoising the t-th frame three-dimensional target frame track by using a constant velocity Kalman filtering algorithm and obtaining a real track set
Then, predicting to obtain a predicted track set
Wherein
A set of predicted trajectories representing a t +1 th frame;
and S3, performing data association on the predicted track and the three-dimensional target frame of the current frame by using the Hungarian algorithm and updating the Kalman filter. The specific process is as follows:
collecting the three-dimensional target frame D of the current t frametPrediction track set T obtained by Kalman filtering algorithmt pThe Hungarian algorithm is input to obtain a data association result, and a three-dimensional target frame set D is obtained in the data association resulttDivided into two sets
Respectively representing a matched three-dimensional detection frame set and an unmatched three-dimensional target frame set by using
Update, for collections
Step S1 is executed to initialize the three-dimensional target trajectory, and in the result of data association, the unmatched trajectory is discarded and the matched trajectory is retained.
And S4, using the denoised three-dimensional target frame sequence to train a long-time memory network LSTM. The specific process is as follows:
setting the time step length L of the LSTM, taking the L as 30, wherein the video frame rate is 30 frames per second, cutting the tracks in the track set according to the frame number L +1, discarding the tracks with the length L +1, taking the three-dimensional target frame sequence of the previous L frames as the input of the LSTM, taking the last frame as the label of the LSTM, and training the LSTM to obtain the three-dimensional target track prediction model.
And step S5, tracking and predicting the three-dimensional target by using a constant-rate Kalman filtering algorithm and a Hungarian algorithm and a trained LSTM, if each frame is subjected to three-dimensional target frame detection, because the mainstream three-dimensional target detector generally has the problem of low detection speed, a three-dimensional target detection result can be obtained every F frames, wherein F is taken as 5, and the middle F frame is predicted by using an LSTM model, so that the tracking result is smoother and the speed is increased. The specific process is as follows:
setting an interval frame number F, if the current track frame number is not equal to N (F +1) +1 and is greater than L, starting an LSTM network to predict a three-dimensional target frame of the next frame, wherein N is a natural number, acquiring the three-dimensional target frame every other F frames, performing target tracking on a three-dimensional target frame sequence of the interval F frames by adopting a constant rate Kalman filtering algorithm and a Hungarian algorithm, namely executing the steps S1 to S3 to obtain a de-noised three-dimensional target frame, and merging the prediction results of the two models into a final tracking result.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (5)
1. A three-dimensional multi-target tracking method based on Kalman filtering and LSTM is characterized by comprising the following steps:
s1, performing track initialization on the input three-dimensional target frame, and determining whether a track is newly established according to whether the three-dimensional frame of the t +1 th frame is matched with the three-dimensional frame of the t th frame or not through initialization;
s2, updating and denoising the t frame three-dimensional target frame track by using a constant velocity Kalman filtering algorithm to obtain a real track set, and predicting to obtain a predicted track set, wherein the predicted track set represents a predicted track set of a t +1 frame;
s3, performing data association on the predicted track and the three-dimensional target frame of the current frame by using the Hungarian algorithm and updating a Kalman filter;
s4, using the denoised three-dimensional target frame sequence for training a long-time memory network LSTM;
and S5, tracking and predicting the three-dimensional target by using a constant velocity Kalman filtering algorithm and a Hungarian algorithm and the trained LSTM.
2. The kalman filtering and LSTM based three-dimensional multi-target tracking method according to claim 1, wherein the track initialization process of step S1 is as follows:
by using
The ith three-dimensional object bounding box representing the tth frame,
the three-dimensional target frame comprises a camera coordinate system, a three-dimensional target frame, a camera coordinate system and a camera coordinate system, wherein x, y, z, l, w, h and theta respectively represent coordinates of the three-dimensional target frame on an x axis, a y axis and a z axis of the camera coordinate system;
the length, width and height of the three-dimensional target frame and the observation angle of the target, set DtA set of all three-dimensional target bounding boxes representing a t-th frame;
if the intersection ratio is higher than the mean
That is, when the intersection ratio of the ith three-dimensional target frame of the tth frame and the jth three-dimensional target frame of the t +1 th frame is greater than or equal to the threshold value threshold; a new track is created
Where k represents the kth track, and the set of tracks at time T +1 is denoted as Tt+1And discarding the remaining three-dimensional target frame.
3. The kalman filtering and LSTM based three-dimensional multi-target tracking method according to claim 1, wherein the data association of step S3 is as follows:
collecting the three-dimensional target frame D of the current t frametPrediction track set T obtained by Kalman filtering algorithmt pThe Hungarian algorithm is input to obtain a data correlation result;
in the result, three-dimensional target frame set DtDivided into two sets
Respectively representing a matched three-dimensional detection frame set and an unmatched three-dimensional target frame set by using
Update, for collections
Executing step S1 to initialize the three-dimensional target track;
in the result of data association, the unmatched traces will be discarded and the matched traces will be retained.
4. The Kalman filtering and LSTM-based three-dimensional multi-target tracking method according to claim 1, wherein the training long-term memory network LSTM of step S4 is as follows:
and setting a time step L of the LSTM, cutting the tracks in the track set according to the frame number L +1, discarding the tracks with the insufficient length L +1, taking the three-dimensional target frame sequence of the previous L frames as the input of the LSTM, taking the last frame as the label of the LSTM, and training the LSTM to obtain the three-dimensional target track prediction model.
5. The kalman filtering and LSTM based three-dimensional multi-target tracking method according to claim 1, wherein the tracking and predicting of the three-dimensional target of step S5 is specifically as follows:
setting an interval frame number F, if the current track frame number is not equal to N (F +1) +1 and is greater than L, starting an LSTM network to predict a three-dimensional target frame of the next frame, wherein N is a natural number, acquiring the three-dimensional target frame every other F frames, performing target tracking on a three-dimensional target frame sequence of the interval F frames by adopting a constant rate Kalman filtering algorithm and a Hungarian algorithm, namely executing the steps S1 to S3 to obtain a de-noised three-dimensional target frame, and merging the prediction results of the two models into a final tracking result.
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| CN112965494A (en) * | 2021-02-09 | 2021-06-15 | 武汉理工大学 | Control system and method for pure electric automatic driving special vehicle in fixed area |
| CN113763434A (en) * | 2021-09-26 | 2021-12-07 | 东风汽车集团股份有限公司 | Target trajectory prediction method based on Kalman filtering multi-motion model switching |
| CN116228989A (en) * | 2023-03-30 | 2023-06-06 | 北京数原数字化城市研究中心 | Three-dimensional track prediction method, device, equipment and medium |
| CN117192063A (en) * | 2023-11-06 | 2023-12-08 | 山东大学 | Water quality prediction method and system based on coupled Kalman filtering data assimilation |
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