Object tracking algorithm based on Camshift algorithm which is combinating with difference in frame is put forward to quickly and exactly track movement object. Firstly, we confirm the movement region of object by difference in frame, and we again confirm centroid of movement region which is centered to initialize tracking curve. Secondly, the feature is abstracted in the region and the target is tracked by Camshift algorithm. The method overcomes shortcoming of artificial orientation and divergence problem for traditional Camshift during tracking. We prove the validity of the algorithm by experiment at last.

In this paper it will introduce the improved Camshift algorithm based on the Kalman filter, which is applied to track a moving target. Improved Camshift algorithm is a algorithm that H, S and V components space is used to track a moving target in HSV space. The next position of the moving object is estimated by the Kalman. According to H and S and V, it can be appropriate to determine the track of moving target. The algorithm effectively overcomes the shortcoming of the traditional Camshift algorithm. For example, it is easy to solve the divergence problem during the tracking. Finally, the usefulness of algorithm is validated by experiment.

The vein recognition was introduced simply at first. Then the relatively important part image processing in recognition process was discussed deeply. Every method was studied and compared with each other. At last, the best scheme was found out for the hand vein recognition. A new segmentation method based on boundary was proposed for infrared imagery and we get the better pretreatment effect

We propose a more effective tracking algorithm which can work robustly in a complex scene such as illumination, appearance change, and partial occlusion. The algorithm is based on an improved particle filter which used the efficient design of observation model. Predefined convolutional filters are used to extract the high-order features. The global representation is generated by combining local features without changing their structures and space arrangements. It not only increases the feature invariance, but also maintains the specificity. The extracted feature from convolution network is introduced into particle filter algorithm. The observation model is constructed by fusing the color feature of the target and a set of features from templates which are extracted by convolutional networks without training in our paper. It is fused with the features extracted from convolutional network for tracking. In the process of tracking, the template is updated in real time, and then the robustness of the algorithm is improved. Experiments show that the algorithm can achieve an ideal tracking effect when the targets are in a complex environment.

The standard particle filter usually fails in the scene of small system noise or weak dynamic models. The robustness of majority tracking algorithm is not high due to using only a single target feature. An efficient multi-feature fusion tracking method was proposed. The article presents the integration of color distributions into kernel particle filtering(KPF) framework, which has typically been used in combination with edge-based image features. The KPF invokes kernels to form a continuous estimate of the posterior density function. Kernel particle filter reasonably allocated particles by improving sampling efficiency. Experiments results show that other features can still stable and reliable track targets when a feature loses identification capabilities of target in the background clutter. Algorithm is simple and high robustness. It can be effectively applied to track target in the complex context.