Deep Similarity Metric Learning for Real-Time Pedestrian Tracking

Understand

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.