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Pedestrian re identification (Reid) - Overview
2022-07-06 15:09:00 【gmHappy】
What is? Re-ID?
- Pedestrian recognition (Person re-identification, abbreviation Re-ID) Also known as pedestrian recognition , Is the use of computer vision technology to determine whether there is a specific pedestrian in the image or video sequence . It is widely regarded as a sub problem of image retrieval . Given a monitored pedestrian image , Retrieve the pedestrian image under the cross device . It aims to make up for the visual limitations of the current fixed camera , And can detect with pedestrians / Combination of pedestrian tracking technology , It can be widely used in intelligent video surveillance 、 Intelligent security and other fields .
- As shown in the figure below : There are multiple cameras shooting video sequences in an area ,ReID The requirements of a camera under the interest of pedestrians , Retrieve all pictures of the pedestrian under other cameras .
Why Re-ID?
In surveillance video , Due to the camera resolution and shooting angle , Very high quality face images are usually not available . When face recognition fails ,ReID It has become a very important alternative technology .
Research forms
- Data sets are usually pedestrian images obtained by manual annotation or detection algorithms , At present, it is independent of detection , Pay attention to identification
- Data sets are divided into training sets 、 Verification set 、Query、Gallery
- Train the model on the training set , After getting the model, right Query And Gallery Image feature extraction and similarity calculation in , For each Query stay Gallery Find out before N A similar picture
- Training 、 The identity of the person in the test is not repeated
Two directions
- feature extraction : Learn to be able to cope with the characteristics of people changing under different cameras
- Measure learning : Mapping the learned features to a new space makes the same people closer and different people farther
There are challenges
- Different cameras cause great changes in the appearance of pedestrians ;
- Target occlusion (Occlusion) Some features are lost ;
- Different View,Illumination Differences in characteristics that lead to the same goal ;
- Different target clothes have similar colors 、 Feature approximation leads to a decrease in discrimination ;
Common data set
CUHK03
Market1501
DukeMTMC-reID
MSMT17
Only commonly used data sets are listed here , A more complete data set can be referred to : Person Re-identification Datasets
Commonly used evaluation index
- rank-k: The sorting list returned by the algorithm , front k If the bit is an existing search target, it is called rank-k hit .eg:rank1: The first is the search target rank-1 hit .
- Cumulative Match Characteristic (CMC)
Take a very simple example , Suppose in face recognition , There are 100 personal , Now comes 1 A face to be recognized ( If label by m1), After comparing with the faces in the bottom database, the faces in the bottom database are sorted from high to low , We found that :
If the recognition result is m1、m2、m3、m4、m5……, Now rank-1 The accuracy of is 100%;rank-2 The correct rate of is 100%;rank-5 The correct rate of is 100%;
If the recognition result is m2、m1、m3、m4、m5……, Now rank-1 The accuracy of is 0%;rank-2 The accuracy of is 100%;rank-5 The correct rate of is 100%;
If the recognition result is m2、m3、m4、m5、m1……, Now rank-1 The accuracy of is 0%;rank-2 The accuracy of is 0%;rank-5 The accuracy of is 100%;
Empathy , When there are many faces to be recognized , Take the average . For example, the face to be recognized has 3 individual ( If label by m1,m2,m3), Similarly, there is a score from high to low for everyone's face ,
such as :
Face 1 The result is m1、m2、m3、m4、m5……,
Face 2 The result is m2、m1、m3、m4、m5……,
Face 3 result m3、m1、m2、m4、m5……,
Now rank-1 The accuracy of is (1+1+1)/3=100%;
rank-2 The correct rate of is (1+1+1)/3=100%;
rank-5 The correct rate of is (1+1+1)/3=100%;
such as :
Face 1 The result is m4、m2、m3、m5、m6……,
Face 2 The result is m1、m2、m3、m4、m5……,
Face 3 result m3、m1、m2、m4、m5……,
Now rank-1 The accuracy of is (0+0+1)/3=33.33%;
rank-2 The accuracy of is (0+1+1)/3=66.66%;
rank-5 The correct rate of is (0+1+1)/3=66.66%;
curve: Calculation rank-k Hit rate of , formation rank-acc The curve of , Here's the picture :
- mAP(mean average precision): Reflect the extent to which all the correct pictures in the database of the person who searched are in front of the sorted list , It can be measured more comprehensively ReID Performance of the algorithm . Here's the picture , Suppose the search pedestrian is gallery There is 10 A picture , In the list Middle position (rank) Respectively 1、2、3、4、5、6、7、8、9, be ap by (1/ 1 + 2 / 2 + 3 / 3 + 4 / 4 + 5 / 5 + 6 / 6 + 7 / 7 + 8 / 8 + 9 / 9) / 10 = 0.90;ap large , The search results of this pedestrian are relatively high , For all query Of ap Take the average value to mAP
Generally speaking ,Precision It's the retrieved items ( such as : file 、 Web page, etc ) How much is accurate ,Recall It's how many of the exact entries have been retrieved .
Accuracy rate = The number of positive samples detected / Total number detected
Recall rate = The number of positive samples detected / Number of all positive samples
Let's take a new example .
Suppose there is a search engine , According to search engines , The results are as follows :
Search for 1 The total number of relevant samples is 5 individual : just , just , just , just , just
Rank1 | just | negative | just | negative | negative | just | negative | negative | just | just |
Recall | 1/5=0.2 | 1/5=0.2 | 2/5=0.4 | 2/5=0.4 | 2/5=0.4 | 3/5=0.6 | 3/5=0.6 | 3/5=0.6 | 4/5=0.8 | 5/5=1.0 |
Precision | 1/1=1.0 | 1/2=0.5 | 2/3=0.66 | 2/4=0.5 | 2/5=0.4 | 3/6=0.5 | 3/7=0.42 | 3/8=0.38 | 4/9=0.44 | 7/10=0.5 |
Precision From left to right 1/1, 1/2, 2/3, 2/4… And so on
Search for 2 There are a total of 3 individual , The following are the results returned by the search engine
Rank1 | just | negative | negative | just | just | negative | negative |
Recall | 0.33 | 0.33 | 0.33 | 0.66 | 1 | 1 | 1 |
Precision | 1.0 | 0.5 | 0.33 | 0.5 | 0.6 | 0.5 | 0.43 |
We put each positive sample corresponding to Precision Averaging
Search for 1 Of mAP:mAP = (1/1 + 2/3 + 3/6 + 4/9+ 5/10) / 5 = 0.72
Search for 2 Of mAP: mAP = (1/1 + 2/4 + 3/5) / 3 = 0.63
Holistic mAP = (0.72 + 0.63) /2 = 0.675
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