Visual Tracking Algorithm Based On Feature-Learning And Feature-Imagination

Vision-based object tracking is an active and challenging research topic in the field of computer vision. It focuses on tracking moving objects in the videos by using the computer. Visual tracking has promising research significance and applications in many fields such as intelligent surveillance, image compression and medical diagnosis. Currently, although many methods can achieve object tracking, but there are still many issues to be resolved.This dissertation discusses the algorithms of visual tracking through human visual perception. We analyze the process of human visual tracking by simulating the human visual characteristics of feature-learning and feature-imagination, proposes the concept of feature-learning and feature-imagination in visual tracking. We establish a complete general theoretical system based on feature-learning and feature-imagination (FLFI), which is used to track human body in this dissertation. The visual tracking algorithm based on FLFI integrates the thinking-way of human vision with the traditional visual tracking methods, breaks the current mind-set of visual tracking algorithm designing and has a bright future in both theories and applications. The main tasks and contributions of this thesis are:1. Analyze the human vision intelligence through cognitive science and cognitive psychology. Discuss the object tracking model of human vision, and describe the thinking characteristics of human vision such as attention, learning, memory and imagination. Furthermore, we propose a visual tracking architecture based on FLFI, which integrates learning and imagination with the traditional vision tracking methods2. A feature representation method is given by considering the human vision intelligence. In this method, the concept of state space is led into the traditional vector-based feature representation methods. We propose a method to learn the features of variable-pose object in different states by using dynamic weighting update methods. After that, we give a visual tracking model based on feature- imagination and introduce its general inference and learning methods.3. Based on the current research status and the existing technical methods of human tracking, this dissertation presents a method of real-time human tracking based on FLFI. This method extracts object features by feature-learning at the beginning of tracking, determines occlusions by feature matching and restores object tracking by feature-imagination. Furthermore, this method does not need human pose recognition and object locating under occlusion situations in the process of tracking. It only needs to appoint an initial object state at the beginning of tracking.4. In order to apply the FLFI visual tracking architecture to human tracking more perfectly, this dissertation also makes some researches on human pose recognition and object tracking under occlusions. In the study of human pose recognition, a human pose estimation algorithm based on human head-shoulder segmentation is given. Aimed at upright walking human, this algorithm divides human pose into six states and estimates human pose through the characteristics of 2D imaging of human. To solve the occlusion problem, we propose a method based on combination of histogram matching and local feature matching.At last, we introduce an intelligent surveillance system evolved by members in the vision group of our lab.