In this paper a cheap joint probabilistic data association (CJPDA) with the neural network state filter (NNSF) is presented for tracking multiple targets in low and high cluttered environments. The state update step of the CJPDA filter (CJPDAF) is realized with the NNSF instead of Kalman filter. Through simulation, a comparison is made to show the performance difference between the CJPDA with N...

Most of the correlation filter based tracking algorithms can achieve good performance and maintain fast computational speed. However, in some complicated tracking scenes, there is a fatal defect that causes the object to be located inaccurately. In order to address this problem, we propose a particle filter redetection based tracking approach for accurate object localization. During the trackin...

-Target tracking is one of the major aspects often used in sonar applications, surveillance systems, communication systems, embedded applications etc. To obtain kinematic components of a moving target such as position, velocity, and acceleration, one of the most used approaches in target tracking is stochastic estimation approach. Movement of the target is described by state space dynamic syste...

Discriminative correlation filters (DCF) have recently shown excellent performance in visual object tracking area. In this paper we summarize the methods of updating model filter from discriminative correlation filter (DCF) based tracking algorithms and analyzes similarities and differences among these methods. We deduce the relationship among updating coefficient in high dimension (kernel tric...

In this paper a vehicle tracking algorithm is presented based on the combination of a per pixel background model (an extension of work by Stauffer and Grimson [12]) and a set of single hypothesis foreground models based on a general model of object size, position, velocity, and colour distribution. Each pixel in the scene is thus ‘explained’ as either background, belonging to one of the foregro...

A tracking system with color and contour information is more efficient and robust than one with color or contour only. However, it is difficult to use both color and contour information. In this paper, we present an approach using the particle filter to fuse color and contour cues in tracking. First, we combine color and contour information in a Kalman filter to generate the proposal distributi...

Target detection and tracking is an important problem in the automatic surveillance system. This paper proposes a Combined Gaussian Hidden Markov Model based Kalman Filter (CGHMM-KF) scheme for tracking people in multiple camera sensor network for monitoring and tracking of target (person/vehicle) in secured area. To detect the target under different illumination conditions, HMM with Mixture of...

A novel algorithm, termed a Boosted Adaptive Particle Filter (BAPF), for integrated face detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive particle filtering algorithm and the AdaBoost face detection algorithm. An Adaptive Particle Filter (APF), based on a new sampling technique, is proposed. The APF is shown to yield more accurate estimate...

In this paper, a dynamic model of car motion is proposed in which the turn of the steering wheel and the distance between the front and rear wheel are taken into account. Extended Kalman Filter (EKF) is widely used in visual tracking systems. However, because there is no direct link between the behaviour of the driver who controls the motion of the car and the assumed dynamic model, the traditi...

In radar tracking application, the observation noise is highly non-Gaussian, which is also referred as glint noise. The performance of extended Kalman filter degrades severely in the presence of glint noise. In this paper, an improved particle filter, Markov chain Monte Carlo particle filter (MCMC-PF), is introduced to cope with radar target tracking in glint noise environment. The Monte Carlo ...