Research On Close Range Video Moving Object Detection From Moving Platform

With the development of scientific technology, people nowadays can easily acquire and save a variety of videos. As a result, digital video data has become abundant. Moving object detection is part of video content analysis, and plays an important role in both scientific research and engineering applications. Especially, moving object detection in close range videos from moving platform has received more and more attention. Due to the combined effects of variations of object depth, stationary or dynamic occlusion between multiple objects, unpredictable scene structure, object as well as camera movement. The two typical groups of method on moving object detection from moving platform are methods based on motion analysis and methods based on statistical learning. In order to dicover and localize moving objects in close range videos from moving platform, this paper focus on the use of local invariant features to represent the video data, the use of motion analysis strategy and statistical learning strategy to analyze the video data.In video processing based on local invariant features, we put emphasis on video content representation by local features. (1) We proposed a novel spatial-temporal context based on spatial neighbor and temporal similiarity to enhance the description of local video features. (2) We proposed to describe local features in the spatial image pyramid in order to capture global configuration of different object parts. Both local global clues are used for object description. (3) We proposed a novel closed loop mapping feature matching method for video feature matching. At the same level of reliability, our method can obtain more matches.In moving object detection based on motion analysis, we proposed a novel moving object detection method based on multiview geometric constraints. The major contributions are: (1) We proposed a new multiview epipolar constraints based on consecutive positions of binocular camera with non-parallel configurations. It can be used for moving object detection when the object and the camera move in the same direction, where the epipolar geometry fails. (2) We proposed to detect and track multiple moving objects under the framework of particle filter so as to deal with the situation of multiple moving objects entering or leaving field of view.In moving object detection based on unsupervised statistical learning, we proposed a novel moving object detection algorithm based on dynamic topic discovery. Taking advantage of a robust representation of video by spatial-temporal context words and an unsupervised learning strategy, we proposed to use dynamic topic modeling to discover and localize moving objects in close range videos from moving platform. In essence, our model consists of two levels: (1) At the feature level, distinctive video patches which are robust to position, scale and lighting variations are extracted. These patches contain important information of different class of moving objects. (2) At the object level, structure and motion similarity across frames are used to build the object model.In moving object detection based on supervised statistical learning, we proposed a novel infrared pedestrian detection algorithm based on discriminative models, as an instance of specified category moving object detection. The major innovations are: (1) We proposed a novel feature centric sliding window region of interest extraction methods. It performs robustly under different sceniarios while greatly reduces the computation cost. (2) We proposed a novel pyramid binary pattern (PBP) feature for infrared person description. Our PBP feature combines both local texture and global shape information and it has been extended to 3D form for pedestrian description.