Download e-book for iPad: Analyzing Video Sequences of Multiple Humans: Tracking, by Jun Ohya, Akira Utsumi, Junji Yamato

By Jun Ohya, Akira Utsumi, Junji Yamato

ISBN-10: 1461353467

ISBN-13: 9781461353461

ISBN-10: 1461510031

ISBN-13: 9781461510031

Analyzing Video Sequences of a number of people: monitoring, Posture Estimation and behaviour Recognition describes a few laptop vision-based tools that study video sequences of people. extra in particular, tools 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 via 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 ensure postures of a human physique, in 3D in real-time. Human actions are famous from a video series through the HMM (Hidden Markov Models)-based technique that the authors pioneered. The effectiveness of the 3 tools is proven by means of experimental results.

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Extra info for Analyzing Video Sequences of Multiple Humans: Tracking, Posture Estimation and Behavior Recognition

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In addition, non-synchronous observations also have several benefits for implementation. In particular, we do not need any mechanism for synchronization. This is especially useful when the observation nodes are placed very far apart. Furthermore, we do not need to be concerned about the processing speed of each observation node. Generally, the processing time for one image can vary depending on the content of the image as well as on the performance of the processor used for the observation node.

If the number of considered observations M were to increase, the possible combinations among candidates would easily explode and it would become difficult for the discovering process to finish in a reasonable time. To avoid this, we would have to reduce the number of observations considered by assuming that the number of new targets simultaneously appearing in a scene is small. In the implementation described below, we use M = 5. ( .... 7. 5. 1. +~a ) Kalman-filtering-based matching IMPLEMENTATION System Overview In this section, we demonstrate an example of fully random observations in a human tracking task.

As mentioned above, our algorithm works adaptively for an input image sequence and produces a stochastic image regarding the target's presence. In the next section, we implement this algorithm in some definite image features. , coarse segmentation). Our system has a model (knowledge) that considers the target size and motion. When an extracted result matches this model, the corresponding region is determined to be the target region T'. 4 shows the extraction process. Let \It = {vI,tll En} be an input image at time t.

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Analyzing Video Sequences of Multiple Humans: Tracking, Posture Estimation and Behavior Recognition by Jun Ohya, Akira Utsumi, Junji Yamato


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