Deep Active Learning of Object Detection for Smart City
Abstract: Deep learning networks are nowadays a major asset for smart city applications and brand new technologies. It is well known that deep learning methods require a great amount of data to have good performance, especially for safety-critical applications such as autonomous driving. Therefore reducing the expensive and time-consuming labelling task done by human annotators is a hot topic. Being one of the most promising candidates to solve this problem, active learning aims to reduce drastically the number of samples to annotate for the learning process. In this work, we focus on the design of an active learning strategy in the specific context of object detection in videos. Besides traditional criteria of sampling, the queries are evaluated based on the temporal coherence of the network’s predictions. Introduced very recently, this characteristic has proven itself to be efficient for evaluating the informativeness of data points. Introducing Temporal Flow, we tested our sampling strategy against the state of the art methods and outperformed them on a benchmark dataset. Indeed, our active learning showed better average performance per labelled samples after each cycle of training. The promising results are encouraging to pursue the effort done in active learning for object detection in videos. A real implementation of this work is feasible but also more research can follow as we acknowledge that further improvements are possible.
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