Joint Learning of Siamese CNNs and Temporally Constrained Metrics for Tracklet Association

TL;DR

This paper introduces a joint learning framework combining Siamese CNNs and temporally constrained metrics for improved multi-object tracklet association in complex scenes, demonstrating superior performance on multiple datasets.

Contribution

It presents a novel online joint learning approach for appearance-based tracklet affinity models using Siamese CNNs and temporal constraints, with a new dataset for evaluation.

Findings

Outperforms state-of-the-art methods on five public datasets.

Effectively handles challenging tracking scenarios.

Introduces a new large-scale annotated dataset for tracking.

Abstract

In this paper, we study the challenging problem of multi-object tracking in a complex scene captured by a single camera. Different from the existing tracklet association-based tracking methods, we propose a novel and efficient way to obtain discriminative appearance-based tracklet affinity models. Our proposed method jointly learns the convolutional neural networks (CNNs) and temporally constrained metrics. In our method, a Siamese convolutional neural network (CNN) is first pre-trained on the auxiliary data. Then the Siamese CNN and temporally constrained metrics are jointly learned online to construct the appearance-based tracklet affinity models. The proposed method can jointly learn the hierarchical deep features and temporally constrained segment-wise metrics under a unified framework. For reliable association between tracklets, a novel loss function incorporating temporally constrained multi-task learning mechanism is proposed. By employing the proposed method, tracklet association can be accomplished even in challenging situations. Moreover, a new dataset with 40 fully annotated sequences is created to facilitate the tracking evaluation. Experimental results on five public datasets and the new large-scale dataset show that our method outperforms several state-of-the-art approaches in multi-object tracking.