1 research outputs found
A Video Analysis Method on Wanfang Dataset via Deep Neural Network
The topic of object detection has been largely improved recently, especially
with the development of convolutional neural network. However, there still
exist a lot of challenging cases, such as small object, compact and dense or
highly overlapping object. Existing methods can detect multiple objects
wonderfully, but because of the slight changes between frames, the detection
effect of the model will become unstable, the detection results may result in
dropping or increasing the object. In the pedestrian flow detection task, such
phenomenon can not accurately calculate the flow. To solve this problem, in
this paper, we describe the new function for real-time multi-object detection
in sports competition and pedestrians flow detection in public based on deep
learning. Our work is to extract a video clip and solve this frame of clips
efficiently. More specfically, our algorithm includes two stages: judge method
and optimization method. The judge can set a maximum threshold for better
results under the model, the threshold value corresponds to the upper limit of
the algorithm with better detection results. The optimization method to solve
detection jitter problem. Because of the occurrence of frame hopping in the
video, and it will result in the generation of video fragments discontinuity.
We use optimization algorithm to get the key value, and then the detection
result value of index is replaced by key value to stabilize the change of
detection result sequence. Based on the proposed algorithm, we adopt wanfang
sports competition dataset as the main test dataset and our own test dataset
for YOLOv3-Abnormal Number Version(YOLOv3-ANV), which is 5.4% average
improvement compared with existing methods. Also, video above the threshold
value can be obtained for further analysis. Spontaneously, our work also can
used for pedestrians flow detection and pedestrian alarm tasks