Paper: Deep Similarity Metric Learning for Real-Time Pedestrian Tracking
Tracking by detection is a common approach to solving the Multiple Object Tracking problem. In this paper we show how learning a deep similarity metric can improve three key aspects of pedestrian tracking on a multiple object tracking benchmark.
We train a convolutional neural network to learn an embedding function in a Siamese configuration on a large person re-identification dataset.
The offline-trained embedding network is integrated in to the tracking formulation to improve performance while retaining real-time performance. The proposed tracker stores appearance metrics while detections are strong, using this appearance information to: prevent ID switches, associate tracklets through occlusion, and propose new detections where detector confidence is low.
This method achieves competitive results in evaluation, especially among online, real-time approaches. We present an ablative study showing the impact of each of the three uses of our deep appearance metric.
Michael Thoreau, Navinda Kottege. Deep Similarity Metric Learning for Real-Time Pedestrian Tracking. In: Australasian Conference on Robotics and Automation; 9 December 2019; Adelaide, Australia. ARAA; 2019. 7.
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