Video analytics of video analytics problems

Lighting changes in the actual environment, target motion complexity, occlusion, target and background color similarity, cluttered background, etc. will increase the difficulty of target detection and tracking algorithm design, the difficult problems are mainly in the following areas:

Complexity of the background: changes in illumination caused by the changes in the target color and the background color changes, which may result in false detection and incorrect tracking. The use of different color space can reduce the impact of lighting changes on the algorithm, but can not completely eliminate its impact; the scene of the foreground target and the background of the mutual conversion, and the baggage put down, pick up, vehicle start and stop; target language background color similarity will affect the effect of target detection and tracking; the target shadow and the background color of the difference between the target shadow and the background color is usually detected as the foreground, which brings difficulties in segmentation of the moving target and feature extraction. This makes segmentation and feature extraction of moving targets difficult.

Target feature trade-offs: Sequence images contain a large amount of feature information that can be used for target tracking, such as the target's motion, color, edges, and texture. However, the feature information of the target is generally time-varying, and it is difficult to select the appropriate feature information to ensure the effectiveness of tracking.

Occlusion problem: Occlusion is a difficult problem that must be solved in target tracking. When a moving target is partially or completely occluded, or when multiple targets are occluded from each other, the target is partially invisible, resulting in a lack of target information, which affects the stability of tracking. In order to minimize the ambiguity caused by occlusion, the correspondence between features and targets during occlusion must be handled correctly. Most of the systems generally predict the position and scale of the target by statistical methods, which cannot deal with the more serious occlusion problems well.

Balancing real-time and robustness: sequence images contain a large amount of information, to ensure the real-time requirements of target tracking, it is necessary to choose the algorithm with a small amount of computation. Robustness is another important performance of the target tracking, to improve the robustness of the algorithm is to make the algorithm more adaptable to the complex background, lighting changes and occlusion, which in turn is at the cost of complex operations.