Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor (1504.02340v1)

Abstract: In this paper, we focus on the two key aspects of multiple target tracking problem: 1) designing an accurate affinity measure to associate detections and 2) implementing an efficient and accurate (near) online multiple target tracking algorithm. As the first contribution, we introduce a novel Aggregated Local Flow Descriptor (ALFD) that encodes the relative motion pattern between a pair of temporally distant detections using long term interest point trajectories (IPTs). Leveraging on the IPTs, the ALFD provides a robust affinity measure for estimating the likelihood of matching detections regardless of the application scenarios. As another contribution, we present a Near-Online Multi-target Tracking (NOMT) algorithm. The tracking problem is formulated as a data-association between targets and detections in a temporal window, that is performed repeatedly at every frame. While being efficient, NOMT achieves robustness via integrating multiple cues including ALFD metric, target dynamics, appearance similarity, and long term trajectory regularization into the model. Our ablative analysis verifies the superiority of the ALFD metric over the other conventional affinity metrics. We run a comprehensive experimental evaluation on two challenging tracking datasets, KITTI and MOT datasets. The NOMT method combined with ALFD metric achieves the best accuracy in both datasets with significant margins (about 10% higher MOTA) over the state-of-the-arts.

• The paper introduces the Aggregated Local Flow Descriptor (ALFD) as a robust affinity measure that leverages long-term motion patterns for effective multi-target tracking.
• It presents a Near-Online Multi-target Tracking (NOMT) algorithm that integrates dynamic, appearance, and trajectory cues for real-time data association.
• Experimental evaluations on KITTI and MOT Challenge datasets demonstrate a 10% improvement in MOTA and reduced identity switches, highlighting its practical impact on autonomous and surveillance systems.

Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor

In the field of computer vision, the capabilities of multi-target tracking (MTT) systems continue to expand significantly due to innovations in designing robust affinity measures and efficient algorithms. The paper, "Near-Online Multi-target Tracking with Aggregated Local Flow Descriptor," contributes strategically to this space by addressing the critical challenge of data association in MTT through the introduction of the Aggregated Local Flow Descriptor (ALFD) and a Near-Online Multi-target Tracking (NOMT) algorithm.

The central challenge in MTT is the accurate association of object detections across frames. This paper introduces the ALFD, an innovative measure that enhances the robustness of detection associations by leveraging long-term interest point trajectories (IPTs). By encoding the relative motion patterns between detections in a descriptor, ALFD provides a robust metric for estimating the likelihood of a match regardless of context. This feature distinctively outperforms conventional spatial and appearance-based affinity metrics, as validated by the quantitative analysis showing approximately 10% improvement in MOTA on challenging datasets.

Concurrently, the NOMT algorithm is proposed to enhance the tracking performance by formulating the association problem as a data-association problem solved repeatedly at every frame. This approach integrates multiple cues— ALFD, target dynamics, appearance similarity, and long-term trajectory regularization— to construct a comprehensive tracking framework. The algorithm operates within a temporal window, allowing for near-online tracking, which enables modifications to past associations as more data is accumulated. This not only ensures improved accuracy but also adheres to causality essential for real-time operations.

Significant numerical results substantiate the claims of improved accuracy through comprehensive evaluations on the KITTI and MOT Challenge datasets. The algorithm's general effectiveness across varied scenarios—such as static and mobile camera setups, and diverse object types—demonstrates its versatility. With the NOMT algorithm, the paper reports substantial gains over state-of-the-art methods, with enhanced recall, precision, as well as reduced identity switches and fragmentations. Such improvements in metrics are pivotal for applications in autonomous systems and surveillance.

Theoretically, the integration of IPTs into an efficient descriptor and the real-time application of an optimized near-online algorithm holds implications for further research in tracking. The ALFD's applicability across scenarios suggests room for adaptation in other computer vision tasks where temporal information and motion patterns are crucial. Furthermore, the synergistic effect of integrating diverse cues into a single framework exemplified by NOMT prompts future exploration into more sophisticated signal fusion techniques.

Practically, the results imply significant advancements for systems requiring reliable object tracking like autonomous vehicles, which necessitate real-time processing, and surveillance systems that demand high accuracy even in cluttered environments. The potential for ALFD's adaptability to other vision tasks, such as action recognition, offers a spectrum of future application domains.

Overall, the work presented in this paper marks a meaningful stride in the domain of multi-target tracking by resolving key challenges with innovative methodologies. It sets the groundwork for future advancements and opens avenues for the exploration of more adaptive and intelligent tracking systems.