By Jun Ohya, Akira Utsumi, Junji Yamato
Analyzing Video Sequences of a number of people: monitoring, Posture Estimation and behaviour Recognition describes a few machine vision-based equipment that examine video sequences of people. extra in particular, equipment for monitoring a number of people in a scene, estimating postures of a human physique in 3D in real-time, and spotting a person's habit (gestures or actions) are mentioned. For the monitoring set of rules, the authors built a non-synchronous procedure that tracks a number of individuals through exploiting a Kalman clear out that's utilized to a number of video sequences. For estimating postures, an set of rules is gifted that locates the numerous issues which make sure postures of a human physique, in 3D in real-time. Human actions are well-known from a video series via the HMM (Hidden Markov Models)-based process that the authors pioneered. The effectiveness of the 3 equipment is proven by means of experimental results.
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Additional resources for Analyzing Video Sequences of Multiple Humans: Tracking, Posture Estimation and Behavior Recognition
In addition to position tracking, we implemented a simple motion state detection process. ) of each person. This motion state detection is mainly based on human height observations. More specifically, if the height becomes significantly small, the system determines the state of 'sitting'. ' These two states are separated based on the target motion. The detection should be based on primitive 2-D image processing, but the current implementation simply utilizes motion vector X to distinguish these two states.
1 .... 16. Tracking accuracy for non-linear motion (circular motion) Furthermore, in the above experiments, tracking errors were less than 10 cm for most of the subjects for both straight and circular motions. This value is significantly smaller than the target (human body) width (of about 50-60 cm). This means that our system is accurate enough to be used for a human surveillance system, which is one of the major applications of human tracking systems. From the above experiments, it can be seen that the proposed method can provide stable tracking of human motion by using non-synchronous observations.
150 . _--'-_--'-_ _. 1 . . 12. 13. 14. 300 400 X[cm) 500 600 Tracking results for two persons positions between succeeding frames, so the gap from the motion model can be considered a transition error. We measured the tracking accuracy of our system for circular human motion by performing experiments for five subjects (subjects A - E) with six cameras. Each subject walked on a circle with a radius of 75 cm. As described above, the performance of each observation node was around 5 Hz. 0 sec. 16 shows the distances from the center of the circle for all subjects in the trajectories detected by the tracking process (averages and variances).