Human fall detection based on body posture spatiotemporal evolution Abnormal falls in public places have significant safety hazards and can easily lead to serious consequences, such as trampling by people. Visiondriven fall event detection has the huge advantage of being noninvasive. However, in actual scenes, the fall behavior is rich in diversity, resulting in strong instability in detection. Based on the study of the stability of human body dynamics, the article proposes a new model of human posture representation of fall behavior, called the xe2x80x9cfivepoint inverted pendulum modelxe2x80x9d, and uses an improved twobranch multistage convolutional neural network (MCNN) to extract and construct the inverted pendulum structure of human posture in realworld complex scenes. Furthermore, we consider the continuity of the fall event in time series, use multimedia analytics to observe the time series changes of human inverted pendulum structure, and construct a spatiotemporal evolution map of human posture movement. Finally, based on the integrated results of computer vision and multimedia analytics, we reveal the visual characteristics of the spatiotemporal evolution of human posture under the potentially unstable state, and explore two key features of human fall behavior: motion rotational energy and generalized force of motion. The experimental results in actual scenes show that the method has strong robustness, wide universality, and high detection accuracy.

An adaptive approach to human motion tracking from video Vision based human motion tracking has drawn considerable interests recently because of its extensive applications. In this paper, we propose an approach to tracking the body motion of human balancing on each foot. The ability to balance properly is an important indication of neurological condition. Comparing with many other human motion tracking, there is much less occlusion in human balancing tracking. This less constrained problem allows us to combine a 2D model of human body with image analysis techniques to develop an efficient motion tracking algorithm. First we define a hierarchical 2D model consisting of six components including head, body and four limbs. Each of the four limbs involves primary component (upper arms and legs) and secondary component (lower arms and legs) respectively. In this model, we assume each of the components can be represented by quadrangles and every component is connected to one of others by a joint. By making use of inherent correlation between different components, we design a topdown updating framework and an adaptive algorithm with constraints of foreground regions for robust and efficient tracking. The approach has been tested using the balancing movement in HumanEvaIII dataset. The average tracking time is under one second, which is much shorter than most of current schemes.