Embedded Tracking Algorithm Based on Multi-Feature Crowd Fusion and Visual Object Compression

The accuracy and poor real-time performance of moving objects in a dynamic range complex environment become the bottleneck problem of the target location and tracking. In order to improve the positioning accuracy and the quality of tracking service, we propose an embedded tracking algorithm based on multi-feature fusion and visual object compression. On the hand, according to the feature of the target, the optimal feature matching method is selected, and the multi-feature crowd fusion location model is proposed. On the other hand, to reduce the dimension of the multidimensional space composed of the moving object visual frame and the compression of the visual object, the embedded tracking algorithm is established. Experimental results show that the proposed tracking algorithm has high precision, low energy consumption, and low delay.