The Study Of Target Tracking Algorithm Based On Improved Particle Filter

Target tracking is the core components of intelligent systems to determine location, movement and identity targets, which is widely used in the field of video surveillance, security systems and intelligent transportation system. It is really a tough job to realize this system and find a widely used and high robust tracking algorithm due to camera movement, target instability, complexity of background and moving similarity. It is hard to find. Kalman filter algorithm, proposed by Mr. Kalman more than 40 years ago, is the best way to solve the problem in the linear Gaussian environment. However, in order to meet technology and application needs, there has been an emergence of studying the nonlinear non-Gaussian filtering algorithm recent years.First, this thesis introduced the common target tracking filter algorithm proposed recently, such as Kalman Filter (KF), extended Kalman filter (EKF), Unscented Filter (UKF) and particle filter (PF) algorithm. Simple and elegant, KF is the best recursive Bayesian estimator in linear Gaussian environment. By using Taylor series, EKF transforms nonlinear problem into linear space, then using Kalman filter to estimate the results to achieve the first order accuracy. Through the fixed sample set to approximate the probability distribution of the state, UKF is better than the EKF on precision and quantity. Nonetheless, as using Gaussian posterior probability density to approximate the system state, it is poor to perform in complex environment. Particle filter (PF) is a Bayesian filtering adopted by Monte Carlo sampling method. The complex target state distribution is expressed as a set of weights (called particle) in this filter. By finding the largest weight particles in the particle filter to determine the most likely target has been proved as the best way to track target in a complex environment. By measuring the nonlinear model (tangent), this thesis demonstrated that the particle filter has the most outstanding performance dealing with the nonlinear situations, and UKF has superior performance than EKF, EKF is better than KF, which is identical to the theoretical analysis.Second, it is a tough job to select the characteristics of targets in target tracking system. If targets have more features, tracking accuracy could be effectively improved, however, computing quantity and calculation time would also increasing. It is imperative for us to take compromise of real-time and accuracy. As high stability and low computational characteristics, color histogram feature are becoming a main feature to describe targets. This thesis introduced RGB space, CMYK space, HSV space. All these spaces, HSV space is more suitable for human to perceive color. Also, this model has good linear scalability advantages. However, single color histogram is sensitive to the background illumination and what's more, tracking accuracy could reduced significantly when this system is interfered with similar color objects. By contract, as objects moments feature has structural characteristics, which have the properties of translation, rotation and scale invariant, etc. It is widely used in image matching and gesture recognition.Finally, by combining both properties of target color and moment invariant this thesis presented an improved particle filter based on method, the characteristic of the color histogram is carried out in HSV space. The weights of the particle are determined by application environment. Further, by determined Euclidean distance properties in the process of replacement, poor quality particles were washed out, reliability of particles were increased and the impact of noises were reduced. The experimental results have been demonstrated that this method ameliorated the interference immunity of the single color property for tracking target. In addition, this method also improved the tracking accuracy and robustness while not affecting the real-time characteristics.