Multiple Object Tracking as ID Prediction

TL;DR

This paper introduces MOTIP, a novel end-to-end trainable approach that treats multi-object tracking as an ID prediction task, achieving state-of-the-art results without complex heuristics.

Contribution

It presents MOTIP, a simple, effective method that transforms object association into an ID prediction problem, enabling end-to-end learning and surpassing traditional heuristic-based methods.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Operates effectively using only object-level features.

Eliminates need for handcrafted association heuristics.

Abstract

Multi-Object Tracking (MOT) has been a long-standing challenge in video understanding. A natural and intuitive approach is to split this task into two parts: object detection and association. Most mainstream methods employ meticulously crafted heuristic techniques to maintain trajectory information and compute cost matrices for object matching. Although these methods can achieve notable tracking performance, they often require a series of elaborate handcrafted modifications while facing complicated scenarios. We believe that manually assumed priors limit the method's adaptability and flexibility in learning optimal tracking capabilities from domain-specific data. Therefore, we introduce a new perspective that treats Multiple Object Tracking as an in-context ID Prediction task, transforming the aforementioned object association into an end-to-end trainable task. Based on this, we propose a simple yet effective method termed MOTIP. Given a set of trajectories carried with ID information, MOTIP directly decodes the ID labels for current detections to accomplish the association process. Without using tailored or sophisticated architectures, our method achieves state-of-the-art results across multiple benchmarks by solely leveraging object-level features as tracking cues. The simplicity and impressive results of MOTIP leave substantial room for future advancements, thereby making it a promising baseline for subsequent research. Our code and checkpoints are released at https://github.com/MCG-NJU/MOTIP.