A learning-based approach for leaf detection in traffic surveillance video

Traffic surveillance video is recorded in uncontrolled outdoor scenarios. If the camera view gets obstructed by the leaves, the video will fail to be used in vehicle tracking and recognition. It is required that the traffic video surveillance systems run self-checking in order to evaluate if the camera view is blocked by leaves or not. In view of this, a two-step learning framework is proposed in this paper to automatically determine whether the video is leaf degraded or leaf free. First, the proposed framework exploits the convolutional neural network to learn the discriminative features of leaf particles. Then the trained model is used to detect candidate leaf patches in the image. Second, a probabilistic approach is used to pool decisions of each candidate leaf patch to generate final leaf detection result in the video. Experimental results are provided to demonstrate that the proposed approach can effectively detect leaves in real-world traffic surveillance video.