Multi-person Pose Estimation with Mid-Points for Human Detection under Real-World Surveillance

Training Physical and Geometrical Mid-Points for Multi-person Pose Estimation and Human Detection Under Congestion and Low Resolution

“…The task for evaluating different algorithms in this paper targets on the real industrial problems where people captured are congested and with low resolution. One research by Gkioxari et al [8] and our previous work [15] indicated that under such situations, top-down human detectors tend to fail in both human detection and pose estimation. Instead of top-down methods, in this paper, we investigated how bottom-up methods could be used in solving human detection problems.…”

“…This research is an extended version of our previous work [15]. In order to find solutions for real industrial problems, we have been focusing on following ways of thinking: (i) Using pose estimation for human detection in order to deal with occlusion in congested situation and to improve the confidence of detected person under low image resolution.…”

“…The process of generating basic patterns follows the method described in our previous research [15]. As to associating the detected body region points, in this paper, we proposed a novel method.…”

“…Figure 7 shows an example of ellipse area. Following our previous research [15], in this research, R major of the ellipse area is set to |(N i , N j )| × 0.35 , and R minor is set to R major × 0.75 . The basic pattern(s) having the most number of valid links will be considered as candidate basic pattern(s).…”

“…The data flow in NeoPose (Fig. 3) follows our previous work [15], where a structure called basic pattern is generated for each person in the image after different types of body region points are detected through a deep neural network. A basic pattern is a set of body region points including a person's shoulders, ears and neck.…”