Beyond Local Search: Tracking Objects Everywhere with Instance-Specific Proposals

TL;DR

This paper introduces a novel object tracking method that searches the entire frame using high-quality, instance-specific proposals, improving robustness especially for fast or irregularly moving objects.

Contribution

It presents a new tracking approach that moves beyond local search windows, utilizing a small set of high-quality proposals and an adaptive object model for enhanced accuracy.

Findings

Outperforms recent state-of-the-art trackers on benchmarks.

Provides improved robustness for fast and low-frame-rate videos.

Effectively suppresses distractors from background clutter.

Abstract

Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of "high-quality" proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra low-frame-rate videos.