Learning Optimal Parameters for Multi-target Tracking with Contextual Interactions

TL;DR

This paper presents a framework for learning parameters in multi-target tracking that incorporates quadratic interactions, achieving high accuracy with faster algorithms and minimal appearance/motion features.

Contribution

It introduces a structured prediction approach with a novel greedy algorithm for quadratic multi-target tracking, matching LP relaxation accuracy but with significantly improved speed.

Findings

Greedy algorithms are nearly as accurate as LP relaxation.

Proper parameter learning enables simple models to outperform complex methods.

Achieves competitive results on three challenging benchmarks.

Abstract

We describe an end-to-end framework for learning parameters of min-cost flow multi-target tracking problem with quadratic trajectory interactions including suppression of overlapping tracks and contextual cues about cooccurrence of different objects. Our approach utilizes structured prediction with a tracking-specific loss function to learn the complete set of model parameters. In this learning framework, we evaluate two different approaches to finding an optimal set of tracks under a quadratic model objective, one based on an LP relaxation and the other based on novel greedy variants of dynamic programming that handle pairwise interactions. We find the greedy algorithms achieve almost equivalent accuracy to the LP relaxation while being up to 10x faster than a commercial LP solver. We evaluate trained models on three challenging benchmarks. Surprisingly, we find that with proper parameter learning, our simple data association model without explicit appearance/motion reasoning is able to achieve comparable or better accuracy than many state-of-the-art methods that use far more complex motion features or appearance affinity metric learning.