Face recognition technology is an important part of building a safe city. Through face recognition technology and deep learning algorithms, urban surveillance can store video surveillance data in a structured manner, and analyze and mine key information to achieve pre-emptive prevention. However, in practical scenarios, the camera does not capture clear faces under any circumstances. Because of masks, hats, etc., police and identification systems are unable to determine identity with facial features.
Moreover, in a real scene, a camera often cannot cover all areas, and there is generally no overlap between multiple cameras. Therefore, it is necessary to use full-body information to lock and find people—that is, to achieve cross-camera tracking of pedestrians by using the overall pedestrian characteristics as an important complement to the face. Nowadays, the field of computer vision has begun to gradually carry out research work on the technology of “passenger recognitionâ€.
Person Re-IdenTIficaTIon (ReID), which is literally understood to recognize pedestrians, is a process for establishing correspondence between different pedestrian images captured by non-over lapping cameras. process. When there is no overlap between the camera shooting ranges, the search difficulty increases greatly because there is no continuous information. Therefore, pedestrian recognition emphasizes the retrieval of specific pedestrians in a video across cameras.
The difference between "Pedestrian Re-identification" and "Pedestrian Detection"
If pedestrian detection is to determine whether there is a pedestrian in the image, then pedestrian recognition is to identify all images of a particular person photographed by different cameras. Specifically, it is to give a picture of a person (query image), find one or more pieces belonging to him/her from a plurality of pictures (gallery images), which is a person comparison technique realized by the overall characteristics of pedestrians. .
The difference between the main application areas: "Pedestrian re-identification" is mainly used in criminal investigation work, image retrieval and other aspects. "Pedestrian Detection" is mainly used in related fields such as intelligent driving, assisted driving and intelligent monitoring.
In order to better understand this problem, consider a few additional questions:
1. Can you use face recognition to make heavy recognition?
In theory, it is ok. However, there are two reasons why face recognition is difficult to apply: First, the situation of the back of the head and the side of the face is widespread, and it is difficult to face recognition of a positive face. Secondly, the pixels captured by the camera may not be high, especially in the telephoto camera, the face is likely to have no 32x32 pixels. Therefore, face recognition is likely to be limited in practical re-identification applications.
2. Some people can judge the color of the clothes, but also need to be recognized by pedestrians?
The color of the clothes is indeed an important factor in judging the recognition of pedestrians, but the color alone is insufficient. First of all, there is a color difference between the cameras, and there will be light effects. Secondly, what do people with jerseys (similar in color) do, looking for details, but the statistical characteristics of color histograms ignore the details. Tests on multiple data sets have shown that light color features are difficult to achieve 50% top1 accuracy.
Research Status of Pedestrian Re-identification
The pictures in the pedestrian recognition problem are from different cameras. However, due to the influence of the angle of the different cameras and the environment, the pedestrian recognition problem has the following characteristics:
Since the effective information of the face cannot be used in the actual monitoring environment, the appearance of the pedestrian can only be used for identification. In different cameras, due to changes in scale, illumination and angle, the appearance of the same pedestrian will have a certain degree of change. Due to changes in pedestrian posture and camera angle, the appearance characteristics of different pedestrians may be more similar than the appearance characteristics of the same person in different cameras.
In response to the above characteristics, researchers in the field of computer vision have conducted a lot of research work in recent years.
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