In this paper, we propose a background subtraction algorithm specific for depth videos from RGB-D cameras. Embedded in a people detection framework, it does not classify foreground / background at pixel level but provides useful information for the framework to remove noise. Noise is only removed when the framework has all the information from background subtraction, classification and object tracking. In our experiment, our background subtraction algorithm outperforms GMM, a popular background subtraction algorithm, in detecting people and removing noise.
We present a new evaluation methodology to better evaluate video processing performance. Recent evaluation methods [10], [9], [11] depend heavily on the benchmark dataset. The result may be different if we change the testing video sequences. The difference is mainly due to the video sequence content which usually includes many video processing problems (illumination changes, weak contrast etc.) at different difficulty levels. Hence it is difficult to extrapolate the evaluation result on new sequences.In this paper, we propose an evaluation methodology that help to reuse the evaluation result. We try to isolate each video processing problem and define quantitative measures to compute the difficulty level of a video relatively to the given problem. The maximum difficulty level of the videos at which the algorithm is performing good enough is defined as the upper bound of the algorithm capacity for handling the problem. To illustrate this methodology, we present metrics that evaluate the algorithm performance relatively to the problems of handling weakly contrasted objects and shadows.
This paper presents a controller for background subtraction algorithms to detect mobile objects in videos. The controller has two main tasks.The first task is to guide the background subtraction algorithm to update its background representation. To realize this task, the controller has to solve two important problems: removing ghosts (background regions misclassified as object of interest) and managing stationary objects. The controller detects ghosts based on object borders. To manage stationary objects, the controller cooperates with the tracking task to detect faster stationary objects without storing various background layers which are difficult to maintain.The second task is to initialize the parameter values of background subtraction algorithms to adapt to the current conditions of the scene. These parameter values enable the background subtraction algorithms to be as much sensitive as possible and to be consistent with the feedback of classification and tracking task.
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