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Research Of Key Algorithms In Multi-Camera Relay Object Tracking

Posted on:2015-03-12Degree:DoctorType:Dissertation
Country:ChinaCandidate:X Y SunFull Text:PDF
GTID:1268330431455176Subject:Pattern Recognition and Intelligent Systems
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Driven by the huge demands of security market, video surveillance technology is developing rapidly towards digital, networked, intelligent directions, in which the traditional small scene surveillance with single camera is being substituted by wide range video surveillance with multiple agents (cameras), and this firstly demands relay or continuous tracking of a particular moving object by multiple cameras instead of the traditional single camera tracking with a limited field of view. As the most fundamental and core technology of intelligent video processing system, research on multi-camera target relay tracking is an important topic in computer vision, with strong theoretical and practical values.Although research of multi-camera relay target tracking has made great progress in the past period of time, it is still in the exploratory stage entirely, and there are many problems restricting its development. Firstly, target relay tracking under multiple cameras should based on accurate target tracking within a single camera, and there are many problems and difficulties in single camera object tracking still unresolved, such as how to adapt to changes of external environment (including changes in ambient light, camera movement, noise pollution, background complexity, etc.), how to select features to adapt to internal changes of tracking target (including attitude changes of target, beyond plane rotation of target, hinge and non-rigid of target, etc.), full and part occlusion problem, tracking initialization problem, balance between accuracy and real-time of tracking, balance between adaptability and robustness of target model and so on. Secondly, the use of multiple cameras brings a lot new theoretical and technical issues to the visual target tracking need to be studied and discussed. At present, the key issues in large scale multiple cameras target relay tracking mainly include initial detection and location of interest target, selection of features which can stably and effectively represent target, online learning of target’s appearance model during single camera and inheritance of empirical model among multiple cameras, target handover among multi cameras and so on. Therefore, this paper aims to solve above key problems in multi-camera target relay tracking, takes particular continuous or synergistic tracking of specific moving target within multi-camera as research background, investigates a series of key algorithms including the initialization and location of specific target, steady single camera target tracking method based on multi-cue fusion, moving target tracking method with feature learning and feature inheriting of intra-camera and inter-camera, and multi-camera target handover method based on spatial and temporal progressive matching.The main research contents of this paper are as follow:1. For the present less performed study of automatic capture and location of specific target based on characterization, taking the common target of pedestrian as example, we research the automatic initialization method of target under variable focus and field of view cameras, only based on simple feature description. Firstly, the target adaptive fragment-based color model is generated in line with variable characterizations, and then cascade sliding window method with three levels, aspect ratio, variance and feature model are adapted to achieve target’s automatically initialization, finally, particle filter state estimation tracking is fusion to automatically locate the target. Experimental results show that when the target’s color and texture are relatively simple, this method obtains a good automatic initialization results only with target’s color characteristic description.2. To accurately and steadily tracking the specific object between multiple cameras, the accurate and fast object tracking within a single camera must first be carried out, and multi-cue integration is an effective way to track the target quickly and accurately in single camera. Therefore, an adaptive target fragment method based on color distribution is proposed to handle single-camera internal target tracking problem, in which sub-blocks are integrated into multi-cue particle filter tracking. Adaptive fragment method determines the number of sub-blocks according to the color distribution of target, which improves the adaptability of initial description of target. During particle filter tracking, the weight of each sub-block is dynamically adjusted according to its spatial reliability and all particles’ distribution, and the splitting and merging of sub-bocks are also involved. All above improve the tracking adaptability especially on target attitude changing, occlusion situations. Automatic selection of adaptive fragment threshold also does finally.3. In order to realize continuous tracking of target in a single camera and among multiple cameras, it is optative to learn and inherit some stabilize features of target during tracking. As tracking in a single camera, the stable appearance model obtained by online learning can adapt to changes of environment and target, which helps to achieve stable long-time tracking. As continuous tracking of one target among multiple cameras is concerned, the stable and robust target model learned during tracking in one camera can be inherited to the tracking of the next camera, and no longer need repeating the complex learning process, the next camera can track the target fastly and stably based on the empirical target model of the first camera. To achieve this goal, based on the popular tracking methods with online learning appearance model, an object tracking method with feature learning and inheriting is been researched, in which the feature learning is realized by online Weighted Multiple Instance Learning Boost algorithm (WMIL), and the feature inheriting is implemented by measuring the classification capacity of features and inheriting and preserving some of them with stability and strong classification ability, at last the motion model is carried out by particle filter tracking algorithm. The discriminative appearance model with feature learning and feature inheriting helps particle filter to evaluate particle set more naturally and effectively, and particle filter helps online Weighted Multiple Instance Learning to generate a better representative set of training examples, which improves the efficiency and robustness of single camera tracking and lays a solid foundation for subsequent steady target handover and empirical model inheritance between multiple cameras.4. In relay target tracking of multi-camera surveillance system, target handover, i.e., the target consistent labeling is a key issue that must be addressed. To solve this problem, a target handover method based on spatial-temporal progressive matching among multi-camera with nonoverlapping field of view is researched. Firstly we artificial determine the environmental map and get the spatial-temporal constraints among cameras through offline learning, including enter/leave zones, their spatial transition probabilities and the transfer time probabilities, and then we use these spatial-temporal constraints progressively sample particles to determine the time of target handover, to calculate weights of particles according to the inherited target’s empirical model, which combines bottom-up and top-down research ideas, adjust the number and weights of particles based on the weights of particle sets corresponding with enter zones, and ultimately realize accurate handover of targets.Research works of this paper are useful attempts in multi-camera relay object tracking, especially key algorithms of continuous tracking of a particular interest target among multiple cameras, which focus on improving the robustness and fastness of tracking algorithms in single camera tracking, and providing a new idea for relay target tracking among multiple cameras.
Keywords/Search Tags:Visual target tracking, Multi-camera, Multi-cue integration, Particle filter, Feature learning, Feature inheriting, Object handover
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