Pedestrian Tracking by Probabilistic Data Association and Correspondence Embeddings
This addresses pedestrian tracking for surveillance and autonomous systems, with incremental improvements in specific setups.
The paper tackles pedestrian tracking by combining kinematic and appearance cues, achieving first place on the 3DMOT2015 benchmark with a fixed-camera approach using fine-tuned detection and kinematic-only JIPDA, while finding that deep correspondence embeddings work best for moving-camera scenarios through global nearest neighbor tracking.
This paper studies the interplay between kinematics (position and velocity) and appearance cues for establishing correspondences in multi-target pedestrian tracking. We investigate tracking-by-detection approaches based on a deep learning detector, joint integrated probabilistic data association (JIPDA), and appearance-based tracking of deep correspondence embeddings. We first addressed the fixed-camera setup by fine-tuning a convolutional detector for accurate pedestrian detection and combining it with kinematic-only JIPDA. The resulting submission ranked first on the 3DMOT2015 benchmark. However, in sequences with a moving camera and unknown ego-motion, we achieved the best results by replacing kinematic cues with global nearest neighbor tracking of deep correspondence embeddings. We trained the embeddings by fine-tuning features from the second block of ResNet-18 using angular loss extended by a margin term. We note that integrating deep correspondence embeddings directly in JIPDA did not bring significant improvement. It appears that geometry of deep correspondence embeddings for soft data association needs further investigation in order to obtain the best from both worlds.