| 摘要(英) |
With the increasing popularity of artificial intelligence, deep learning techniques can be used to identify, classify, and detect objects based on image features, which can be used as the basis for abnormal event detection. However, according to surveillance image analysis, the target objects in abnormal events only account for a small part. Uploading all the surveillance images to server for object detection waste a large amount of uplink bandwidth, which indirectly reduces the number of images uploaded to the server for detection. In order to solve the problems above, the research proposes a multi-layer filtering image mechanism to reduce the usage of uplink bandwidth. The first layer of foreground object filter quickly filters out images containing foreground objects to reduce the number of images that target object filter needs to identify. The second target filter filters out the images that do not contain the target object through the object detection model to reduce the amount of uploaded data. In addition, the research adopt multi-thread implementation to improve the execution efficiency of multiple filters and Blocking Queue to synchronize the processing speed between filters. The results found that using dynamic filter threshold in the foreground object filter reduces the filtering error rate. In addition, adding additional target object filter can reduce the filtering error caused by the foreground object filter. In consequence, the mechanism proposed by this research can reduce the usage of uplink bandwidth by about 79％ while maintaining the filtering error rate below 2％. |
| 參考文獻 |
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