Chengbin Zeng

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Reliable and real-time people counting is crucial in many applications. Most previous works can only count moving people from a single camera, which cannot count still people or can fail badly when there is a crowd (i.e., heavy occlusion occurs). In this article, we build a system for robust and fast people counting under occlusion through multiple cameras.(More)
This paper presents a human detection approach which can process images rapidly and detect the objects accurately. The features used in our system are the cascade of the HOG (Histograms of Oriented Gradients) and LBP (Local Binary Pattern). In order to achieve high recall at each stage of the cascade, we modify the mi-SVM (Support Vector Machine for(More)
Human detection has attracted much attention in recent years due to its widespread applications. Most existing multi-camera systems focus on locating moving people in each camera and thus resolve the occlusion, which cannot detect still people in images. To overcome this problem, we extend previous 3D search method from single camera to multi-camera. We(More)
Natural human-computer interaction, as an active research topic in the area of computer vision, has widespread applications. In this paper, we propose a new method using forearm gesture as the input of the Tetris game via an ordinary camera. We first use histograms of oriented gradients and the skin color to detect the player's forearm. Then, the movement(More)
In this paper, we describe FTRDBJ’s systems and experiments for TRECVID2012 SIN task. This year, a new type of runs named “concept pair” was evaluated, for which we tried many different models. We submitted two “full” runs and two “pair” runs. For the “full” runs, our systems are very similar to the old ones used in 2011. A 6feature and a 9-feature(More)
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