LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking

TL;DR

LaSOT is a large, high-quality benchmark dataset with extensive annotations and challenges designed to advance research in large-scale single object tracking, including the integration of natural language features.

Contribution

This paper introduces LaSOT, the largest densely annotated tracking benchmark with natural language annotations, facilitating improved training and evaluation of tracking algorithms.

Findings

35 tracking algorithms evaluated with room for improvement

LaSOT's extensive size and annotations enhance tracking research

Natural language annotations open new research directions

Abstract

In this paper, we present LaSOT, a high-quality benchmark for Large-scale Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M frames in total. Each frame in these sequences is carefully and manually annotated with a bounding box, making LaSOT the largest, to the best of our knowledge, densely annotated tracking benchmark. The average video length of LaSOT is more than 2,500 frames, and each sequence comprises various challenges deriving from the wild where target objects may disappear and re-appear again in the view. By releasing LaSOT, we expect to provide the community with a large-scale dedicated benchmark with high quality for both the training of deep trackers and the veritable evaluation of tracking algorithms. Moreover, considering the close connections of visual appearance and natural language, we enrich LaSOT by providing additional language specification, aiming at encouraging the exploration of natural linguistic feature for tracking. A thorough experimental evaluation of 35 tracking algorithms on LaSOT is presented with detailed analysis, and the results demonstrate that there is still a big room for improvements.