Multiple Moving Targets Tracking Based On AMCMC Algorithm And Feature Extraction

Moving targets tracking has an important application foreground in self-government vehicle navigation, robot control, tracking-based recognition, video compression, vision-based control, man-machine interface, medical imaging, augmented reality and video monitoring. With the extended application, a variety of new technologies have been applied to target tracking in more complex environments. But so far, no algorithm is applicable to all cases, so it is a great challenge to study a robust, precise and high-performance moving targets tracking algorithm. The ultimate goal of moving target tracking is to analyze and understand their behavior and the interactive relationship with other targets. Understanding and describing of behavior of moving objects have attracted great attention of scholars, and have become the most challenging research direction. It is the key issue to push the low-level and medium-level processing of computer vision to high-level abstract thinking.Based on reviewing the previous work, and doing research in a variety of tracking algorithms and their application occasion, the thesis proposes a new tracking algorithm, which can meet the real-time requirement of video surveillance system and can solve tracking problems of interaction, collisions, targets entering and leaving scene, multi-target tracking and so on. Using the proposed algorithm, the thesis successfully tracks a wide range of moving targets (eg, human, fish, birds). Additionally, in the measurement of change of solution concentration using digital holographic interferometry, the thesis achieves noise-free tracking and reconstruction of solution concentration change. For features of targets are main information for high-level research, this thesis extracts main motion features of targets based on tracking.First, an Added Markov Chain Monte Carlo (AMCMC) particle filter algorithm is proposed. Different from the Reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm, we add a new entered and left objects detection part after the MCMC particle filter which makes the sampling process like a MCMC particle filter. The AMCMC algorithm has a low computational complexity, can easily handle the variable dimension state vectors processing, and can define swap move type to resolve problems of interaction and collisions. By defining different observation models and move types in AMCMC algorithm, the thesis achieves tracking of moving people, multiple maneuverable targets fish and birds. In tracking, different observation models are used in different move types to track accurately with low computational complexity.Then, the thesis extracts main motion features of targets. The moving speed and direction of targets are gotten based on the basic size and position features. The find that the size of bird's area in image fluctuates with wing flaps in the same period gives us inspiration for measuring the wing flap frequency by analyzing the size signal of bird's area. Short-Time Fourier Transform (STFT) is used to analyze size signals after a smooth filter and a normalizing filter to reflect local time flap frequencies. The average frequency of every window is computed to get the quantitative local frequency.Finally, for achieving noise-free tracking and reconstruction of solution concentration change, the thesis introduces the process and theory of tracking and reconstruction firstly, and then a similar median filter is used as a post-filter to eliminate the noise on the measurement results.