Beyond Pixels: Leveraging Geometry and Shape Cues for Multi-Object Tracking

BeyondPixels: A simple and effective multi-object tracker

Abstract

This paper introduces geometry and object shape and pose costs for multi-object tracking in urban driving scenarios. Using images from a monocular camera alone, we devise pairwise costs for object tracks, based on several 3D cues such as object pose, shape, and motion. The proposed costs are agnostic to the data association method and can be incorporated into any optimization framework to output the pairwise data associations. These costs are easy to implement, can be computed in real-time, and complement each other to account for possible errors in a tracking-by-detection framework. We perform an extensive analysis of the designed costs and empirically demonstrate consistent improvement over the state-of-the-art under varying conditions that employ a range of object detectors, exhibit a variety in camera and object motions, and, more importantly, are not reliant on the choice of the association framework. We also show that, by using the simplest of associations frameworks (two-frame Hungarian assignment), we surpass the state-of-the-art in multi-object-tracking on road scenes.

Publication
In International Conference on Robotics and Automation
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Sarthak Sharma
Masters Student
Junaid Ahmed Ansari
Masters Student
Krishna Murthy Jatavallabhula
Krishna Murthy Jatavallabhula
PhD Candidate

My research blends robotics, computer vision, graphics, and physics with deep learning.

Madhava Krishna
Professor

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