关键词: YOLOv3模型, 视频目标检测, 互信息熵, 线性迭代, 直方图均衡

Abstract:

Aiming at the disadvantage of YOLOv3 in the field of wildlife video target detection,  it is difficult to describe the relationship between the front and back video frames and the region, the Context-aware YOLO model is proposed. The model uses mutual information entropy to quantize the image similarity of adjacent frames, fits the correlation factor of frame fusion according to the quantization result, and uses the correlation factor to linearly iterate the feature map of the video before and after the frame; the histogram equalization method is introduced to calculate the similarity and judge the situation of “shot switching” to determine the critical condition of feature map fusion. The experimental results show that the Context-aware YOLO model has an increase of  2.4% over the F1 value of the YOLOv3 model, and the average accuracy（mAP） has increased by 4.71%.

Key words: YOLOv3 model, video object detection, mutual information entropy, linear iteration, histogram equalization