CN103258217A - Pedestrian detection method based on incremental learning - Google Patents
Pedestrian detection method based on incremental learning Download PDFInfo
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- 238000001514 detection method Methods 0.000 title claims abstract description 24
- 238000012549 training Methods 0.000 claims abstract description 67
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Abstract
The invention discloses a pedestrian detection method based on incremental learning. The pedestrian detection method based on the incremental learning can be used in the field of pedestrian detection in the background of big data. The pedestrian detection method based on the incremental learning comprises the following steps: firstly, an initial classifier is obtained by utilizing an image sample to intensively train a classifier; secondly, a novel training set is utilized to carry out incremental learning of the classifier, and a renewed classifier is obtained; thirdly, an image to be detected is detected by utilizing the renewed classifier to obtain a pedestrian detection result. The pedestrian detection method based on the incremental learning can occupy little internal storage in short time, and utilizes a plurality of continuously collected training sets in the background of big data to effectively carry out study and renew the classifier. Compared with a common method, the pedestrian detection method based on the incremental learning is high in speed, small in occupied internal storage, and convenient to use, and a classifying effect quite similar to the common method can be learnt.
Description
Technical field
The present invention relates to pattern-recognition and machine learning, particularly a kind of pedestrian detection method of incremental learning.
Background technology
Traditional pedestrian detection method, the feature that normally will extract from image use support vector machine to do training and obtain a sorter.This method is feasible when training sample is few.Under big data background, training data is often very big, and collects successively and obtain.Whenever collect a new training dataset, with regard to need will the data in the former training set and new training set in data merge later the training.Complicated operation all needs to train again support vector machine also to need to consume a large amount of time and memory size at every turn like this.Especially, if training data is big especially, memory size possibly can't satisfy the demand of training support vector machine.
In addition, conventional Incremental Learning Algorithm (as gradient decline at random) learning ability is limited, with the sorter that uses support vector machine to learn no small gap is arranged.Carry out repeatedly after the incremental learning, this gap can constantly add up.
Summary of the invention
In view of this, the present invention proposes a kind of pedestrian detection algorithm based on incremental learning.This method at first uses the little training set training support vector machine of data volume to obtain after the initial sorter, whenever collecting new training set, only needs to use the sorter that obtains before the renewal of incremental learning method to get final product.It is fast to do speed like this, and little to memory demand.Even have the data volume of unlimited training sample, also can do training at the machine that only has conventional memory.
Pedestrian detection method based on incremental learning provided by the invention, it may further comprise the steps:
Step 1, utilize the image pattern training classifier in the training set, obtain initial sorter;
Step 2, utilize new training set to carry out the incremental learning of sorter, the sorter after obtaining upgrading;
Sorter after step 3, utilization are upgraded detects testing image, obtains the pedestrian detection result.
The method according to this invention under big data background, can fast and effeciently be handled the training data for pedestrian detection.Even can on the machine that only has common memory, use close to infinite training data and upgrade sorter.Save the training time greatly, also can take full advantage of all utilizable training datas, thereby improved the classification capacity of sorter.
Description of drawings
Fig. 1 is based on the pedestrian detection method process flow diagram of incremental learning among the present invention.
Fig. 2 is based on the pedestrian detection method process flow diagram of incremental learning in another embodiment of the present invention.
Embodiment
For making the purpose, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and with reference to accompanying drawing, the present invention is described in further detail.
The invention discloses a kind of pedestrian detection method based on incremental learning.Fig. 1 shows among the present invention the pedestrian detection method process flow diagram based on incremental learning.As shown in Figure 1, this method may further comprise the steps:
Step 1, training classifier obtain initial sorter.This step specifically comprises:
Step 11, collect several pedestrian's image and background images, set up pedestrian's database, and described database is divided into training set and test set;
The feature of all images in step 12, the described training set of extraction and the test set; In this step, from training set
In extract the feature of image
Wherein, N1 represents number of samples in first training set, I
iThe image of representing i sample, X
iThe feature of representing i sample, y
iThe label information (pedestrian or non-pedestrian) of representing i sample.The feature of extracting can be the histogram of gradients feature, also can be other features of can be linearly pedestrian and background image being separated.
Step 13, the characteristics of image that will extract from described training set and corresponding classification mark (pedestrian's image tagged is positive sample, and background image is labeled as negative sample) are sent into support vector machine (linear kernel gets final product) and are done training, to obtain initial sorter; Wherein support vector machine is used for making up the classification lineoid for current training sample optimum, the following expression of classification lineoid that it obtains:
Wherein, X is the proper vector of sample, W
0And b
0Be the parameter of the sorter that obtains by support vector machine study, it is pedestrian's image or background image that sorter is used for differentiate between images; Work as f
0(X) value can be judged as this image pedestrian's image, otherwise then be background image more than or equal to 0 the time;
Step 14, the described test set of use are tested sorter.The characteristics of image that soon extracts from test set is imported described sorter, so that the image in the test set is classified, and judges the classification capacity of this sorter; Wherein, to the important evaluation criterion of classification capacity be when ratio that negative sample is divided by mistake be 10,000/time, the ratio that positive sample is divided by mistake; The step of this test is for incremental learning afterwards provides normative reference, that is, under the identical standard, the ratio that positive sample is divided by mistake reduces then for effective after the incremental learning.
Step 2, the new training set of use carry out the sorter incremental learning.This step is used for the sorter that has trained is carried out incremental learning, and to obtain the sorter of better performance, it specifically comprises:
Step 21, new several pedestrians and the background image of collection are set up new training set; In this step, the new training set of collecting is expressed as
Wherein, Nj is the number of samples of j new training set; In principle, in the new training set generally can not with the training set of used training before in identical image is arranged, if it is also passable to have only few images to repeat;
Step 22, the sample order in the described new training set is upset at random, thereby the entanglement randomly of positive sample and negative sample is arranged, strengthen robustness; Extract the feature of image in this new training set; In this step, in the process that positive sample and negative sample order are upset, the mark of sample must remain accurately;
Step 23, an initial attack parameter C is set for incremental learning
INITWith attack parameter attenuation coefficient dr; In this step, attack parameter C
INITBe used for describing " level of learning " in the incremental learning process, C
INITMore big then new samples is more big to the influence of sorter, C
INITMore little then new samples is more little to the influence of sorter; Attack parameter attenuation coefficient dr be one greater than 0 real number, be used for describing the speed that each takes turns attack parameter decay in the iteration, the more big attack parameter C of dr
INITIt is more fast to decay;
Step 24, according to feature and the image category mark thereof of the image that extracts in the described new training set, use online Passive-Aggressive (PA) algorithm that existing sorter (classification lineoid) is upgraded; In this step, use be online Passive-Aggressive algorithm, it is at the inseparable data of linearity, lax is the version of quadratic power; Online Passive-Aggressive algorithm is existed algorithms, is suitable for making up linear model;
Step 25, reduce attack parameter according to described method, and change step 24 and carry out, until convergence or reach the maximum iteration time of appointment, obtain new sorter; In this step, the mode that reduces attack parameter is C=C
INIT/ (k*dr), and wherein, C
INITBe initial attack parameter, it is the k time iteration that k refers to current, and dr is the attack parameter attenuation coefficient.
The new sorter that step 26, use test set pair obtain is tested.
Step 3, utilization are carried out pedestrian detection through the sorter that obtains behind the incremental learning to testing image, obtain testing result.Whether be specially: extracting the feature of testing image, described characteristics of image is inputed to described sorter, is pedestrian's testing result.
In the said method that the present invention proposes, when needs use new training set that sorter is trained, can on the basis of original sorter, upgrade by 2 pairs of original sorters of above-mentioned steps, to obtain new sorter; And do not need as traditional pedestrian detection training method, need original training set and new training set merging are trained later.
In order to verify implementation result of the present invention, the present invention has provided another one embodiment, is the example explanation with INRIA pedestrian's database namely.In the training set of this database, comprise pedestrian's image and 1218 complete background images (from every width of cloth image, taking out the image of 10 width of cloth fixed measures at random as negative sample) of 2416 fixed measures.Pedestrian's image and 403 complete background images of comprising 1126 fixed measures in the test set.In order to describe the detailed process of inventive method in detail, training set (is comprised that positive sample and negative sample are divided into 3 parts (Part I, Part II, Part III) at random.Each part comprises 805 (perhaps 806) individual positive sample and 4060 negative samples.
Fig. 2 shows in another embodiment of the present invention the pedestrian detection method based on incremental learning.As shown in Figure 2, this method specifically comprises the steps:
Step S1, with Part I as training set, with all test sample books as test set;
Step S2 extracts the histogram of gradients feature of image among the S1;
Step S3 uses linear support vector machine to do training the feature extracted among the S2 and the label information of sample thereof, obtains initial sorter;
Step S4 does test with the sorter that obtains among the S3 with test set, calculates the classifying quality of current sorter;
Step S5, with Part II as new training set;
Step S6 upsets sample order in the new training set at random, and calculates the feature of image in this training set;
Step S7 arranges an initial attack parameter C
INITWith attack parameter attenuation coefficient dr;
Step S8 uses online PassiVe-AggressiVe algorithm that sorter is upgraded the feature of new training set and classification mark thereof;
Step S9 reduces attack parameter, and iterative step S8 is (generally being no more than 20 times) for several times, until reaching given number of iterations, obtains new sorter;
Step S10, use test set pair sorter is tested, and calculates the classifying quality of current sorter;
Step S11 with Part III training set, uses step S6-S9 to upgrade sorter, and with the classification capacity of S10 testing classification device.
Above-described specific embodiment; purpose of the present invention, technical scheme and beneficial effect are further described; be understood that; the above only is specific embodiments of the invention; be not limited to the present invention; within the spirit and principles in the present invention all, any modification of making, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (11)
1. pedestrian detection method based on incremental learning, it may further comprise the steps:
Step 1, utilize the image pattern training classifier in the training set, obtain initial sorter;
Step 2, utilize new training set to carry out the incremental learning of sorter, the sorter after obtaining upgrading;
Sorter after step 3, utilization are upgraded detects testing image, obtains the pedestrian detection result.
2. the method for claim 1 is characterized in that, step 1 specifically comprises:
Step 11, collect several pedestrian's image and background images, set up pedestrian's database, and described pedestrian's database is divided into training set and test set;
Characteristics of image and the classification mark of image pattern in step 12, the described training set of extraction; Described classification mark is used for whether this image pattern of mark is pedestrian's image;
Step 13, the characteristics of image that extracts and classification mark are sent into support vector machine do training, obtain the preliminary classification device.
3. the method for claim 1 is characterized in that, step 2 specifically may further comprise the steps:
Step 21, collect several new pedestrians and background image as image pattern, set up new training set;
Step 22, extract characteristics of image and the classification mark of image pattern in the new training set, described classification mark is used for whether this image pattern of mark is pedestrian's image;
Step 23, the initial attack parameter that incremental learning is set and attack parameter attenuation coefficient; Described initial attack parameter is used for the presentation video sample to the weighing factor of sorter, and described attack parameter attenuation coefficient is used for the every attenuation degree of taking turns the iteration attack parameter of expression;
The characteristics of image that step 24, utilization are extracted and the existing sorter of classification flag update of image;
Step 25, reduce attack parameter according to described attack parameter attenuation coefficient, and change step 24 and carry out, until convergence or reach maximum iterations, the sorter after obtaining upgrading.
4. method as claimed in claim 2 is characterized in that, described step 1 also comprises:
Step 14, utilize initial sorter that the image pattern in the described test set is tested, so that the image pattern in the described test set is classified, and then obtain the classification capacity of preliminary classification device.
5. the method for claim 1 is characterized in that, described characteristics of image is for being categorized as image pattern the feature of pedestrian's image and background image linearly.
6. method as claimed in claim 2 is characterized in that, described support vector machine is for being used for making up the classification lineoid to current training image sample optimum, the following expression of described classification lineoid:
Wherein, X is the characteristics of image vector of image pattern, W
0And b
0Be the parameter of the sorter that obtains by support vector machine study, it is pedestrian's image or background image that sorter is used for differentiate between images; Work as f
0(X) value was more than or equal to 0 o'clock, and this image pattern is pedestrian's image, otherwise then is background image.
7. the method for claim 1 is characterized in that, the training set in described step 1 and the step 2 is different with the image pattern in the new training set.
8. method as claimed in claim 3 is characterized in that, extracts in the step 22 before the characteristics of image, and the order of the image pattern in the new training set is upset at random.
9. method as claimed in claim 3 is characterized in that, utilizes the PA algorithm that existing sorter is upgraded in the step 24.
10. method as claimed in claim 3 is characterized in that, utilizes following mode to reduce attack parameter in the step 25:
C＝C
INIT/(k*dr)
Wherein, C
INITBe initial attack parameter, it is the k time iteration that k refers to current, and dr is the attack parameter attenuation coefficient.
11. the method for claim 1 is characterized in that, repeatedly utilizes step 2 pair existing sorter to carry out incremental learning, and then the sorter that obtains upgrading, wherein each training set difference of using of upgrading.
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| CN103473953A (en) * | 2013-08-28 | 2013-12-25 | 奇瑞汽车股份有限公司 | Pedestrian detection method and system |
| CN105184322A (en) * | 2015-09-14 | 2015-12-23 | 哈尔滨工业大学 | Multi-temporal image classification method based on incremental integration learning |
| CN105608179A (en) * | 2015-12-22 | 2016-05-25 | 百度在线网络技术（北京）有限公司 | Method and device for determining relevance of user identification |
| CN106446942A (en) * | 2016-09-18 | 2017-02-22 | 兰州交通大学 | Crop disease identification method based on incremental learning |
| CN107358257A (en) * | 2017-07-07 | 2017-11-17 | 华南理工大学 | Under a kind of big data scene can incremental learning image classification training method |
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| CN103473953A (en) * | 2013-08-28 | 2013-12-25 | 奇瑞汽车股份有限公司 | Pedestrian detection method and system |
| CN105184322A (en) * | 2015-09-14 | 2015-12-23 | 哈尔滨工业大学 | Multi-temporal image classification method based on incremental integration learning |
| CN105184322B (en) * | 2015-09-14 | 2018-11-02 | 哈尔滨工业大学 | A kind of multidate image sorting technique based on incremental integration study |
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| CN105608179A (en) * | 2015-12-22 | 2016-05-25 | 百度在线网络技术（北京）有限公司 | Method and device for determining relevance of user identification |
| CN106446942A (en) * | 2016-09-18 | 2017-02-22 | 兰州交通大学 | Crop disease identification method based on incremental learning |
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| CN110679114A (en) * | 2017-05-24 | 2020-01-10 | 国际商业机器公司 | Method for estimating deletability of data object |
| CN110679114B (en) * | 2017-05-24 | 2021-08-06 | 国际商业机器公司 | Method for estimating deletability of data object |
| CN107358257B (en) * | 2017-07-07 | 2019-07-16 | 华南理工大学 | Under a kind of big data scene can incremental learning image classification training method |
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| CN109091098A (en) * | 2017-10-27 | 2018-12-28 | 重庆金山医疗器械有限公司 | Magnetic control capsule endoscopic diagnostic and examination system |
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| CN109359207A (en) * | 2018-12-24 | 2019-02-19 | 焦点科技股份有限公司 | A kind of Logo detection method being easy and fast to iteration update |
| CN109359207B (en) * | 2018-12-24 | 2021-01-22 | 焦点科技股份有限公司 | Logo detection method easy for quick iterative update |
| CN111488917A (en) * | 2020-03-19 | 2020-08-04 | 天津大学 | Garbage image fine-grained classification method based on incremental learning |
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Application publication date: 20130821 |