An Improved Fast Affine Motion Estimation Based on Edge Detection Algorithm for VVC

As a newly proposed video coding standard, Versatile Video Coding (VVC) has adopted some revolutionary techniques compared to High Efficiency Video Coding (HEVC). The multiple-mode affine motion compensation (MM-AMC) adopted by VVC saves approximately 15%-25% Bjøntegaard Delta Bitrate (BD-BR), with an inevitable increase of encoding time. This paper gives an overview of both the 4-parameter affine motion model and the 6-parameter affine motion model, analyzes their performances, and proposes improved algorithms according to the symmetry of iterative gradient descent for fast affine motion estimation. Finally, the proposed algorithms and symmetric MM-AMC flame of VTM-7.0 are compared. The results show that the proposed algorithms save 6.65% total encoding time on average, which saves approximately 30% encoding time of affine motion compensation.

Multi-Feature Fusion Based Object Detecting and Tracking

A new approach is proposed to detect and track the moving object. The affine motion model and the non-parameter distribution model are utilized to represent the object firstly. Then the motion region of the object is detected by background difference while Kalman filter estimating its affine motion in next frame. Center association and mean shift are adopted to obtain the observation values. Finally, the distance variance and scale variance between the estimated and detected regions are used to fuse the observation values to acquire the measurement value. To correct fusion errors, the observable edges are employed. Experimental results show that the new method can successfully track the object under such case as merging, splitting, scale variation and scene noise.