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VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows (2108.05015v4)

Published 11 Aug 2021 in cs.CV and cs.AI

Abstract: Different from visible cameras which record intensity images frame by frame, the biologically inspired event camera produces a stream of asynchronous and sparse events with much lower latency. In practice, visible cameras can better perceive texture details and slow motion, while event cameras can be free from motion blurs and have a larger dynamic range which enables them to work well under fast motion and low illumination. Therefore, the two sensors can cooperate with each other to achieve more reliable object tracking. In this work, we propose a large-scale Visible-Event benchmark (termed VisEvent) due to the lack of a realistic and scaled dataset for this task. Our dataset consists of 820 video pairs captured under low illumination, high speed, and background clutter scenarios, and it is divided into a training and a testing subset, each of which contains 500 and 320 videos, respectively. Based on VisEvent, we transform the event flows into event images and construct more than 30 baseline methods by extending current single-modality trackers into dual-modality versions. More importantly, we further build a simple but effective tracking algorithm by proposing a cross-modality transformer, to achieve more effective feature fusion between visible and event data. Extensive experiments on the proposed VisEvent dataset, FE108, COESOT, and two simulated datasets (i.e., OTB-DVS and VOT-DVS), validated the effectiveness of our model. The dataset and source code have been released on: \url{https://github.com/wangxiao5791509/VisEvent_SOT_Benchmark}.

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Citations (91)

Summary

  • The paper presents a dual-modality tracking method that fuses RGB frames with event flows to overcome challenges such as motion blur and low illumination.
  • The study introduces a large-scale VisEvent dataset with 820 video pairs for evaluating tracking performance in diverse and challenging conditions.
  • The proposed Cross-Modality Transformer employs cross-attention to refine feature representations, outperforming traditional single-modality trackers.

Analysis of "VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows"

The paper "VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows" presents a comprehensive approach to object tracking by integrating data from both visible and event cameras. The core motivation stems from the complementary capabilities of these two types of sensors. Visible cameras, while effective at capturing detailed textures and slow-moving objects, often fall short under conditions like low illumination, fast motion, or cluttered backgrounds due to issues such as motion blur. Conversely, biologically inspired event cameras excel in such challenging environments due to their asynchronous data capture and higher dynamic range capabilities.

Contributions and Approach

One of the major contributions of this work is the introduction of a large-scale benchmark dataset, termed VisEvent, consisting of 820 video pairs. The dataset is specifically designed to simulate realistic and challenging tracking conditions, making it a valuable asset for the tracking community. It provides a rich set of scenarios, including low illumination and high-speed tracking environments.

The authors propose transforming event flows into event images, a critical step that enables the integration of event data into existing deep learning frameworks designed for frame-based video. They develop more than 30 baseline methods by extending existing single-modality tracking algorithms into dual-modality frameworks, employing a variety of fusion strategies.

Central to their tracking approach is the Cross-Modality Transformer (CMT), a novel fusion module designed to enhance feature learning by facilitating interactions between RGB and event data. This module leverages cross-attention mechanisms for inter-modality feature fusion, followed by self-attention layers that refine intra-modality representations, leading to improved tracking performance.

Experimental Results

The extensive experiments conducted on the VisEvent dataset, along with tests on other datasets such as FE108, COESOT, OTB-DVS, and VOT-DVS, demonstrate the efficacy of their approach. The fusion of visible frames and event flows shows a marked improvement over models relying on a single modality. Tracking robustness is significantly enhanced through the use of the CMT module, which outperforms several state-of-the-art trackers. The results emphasize the potential of combining these divergent sensor modalities for enhanced object tracking.

Implications and Future Directions

The implications of this research are notable, as it paves the way for more reliable tracking in environments previously considered challenging for traditional RGB-based systems. By addressing the long-standing issues of motion blur and poor performance in various lighting conditions, this dual sensor approach offers a pathway toward robust tracking systems suitable for diverse applications such as surveillance, autonomous navigation, and robotics.

For future research direction, the authors recognize the need for further exploration into event representation and temporal information extraction to fully exploit the event data's potential. Moreover, the integration and comparative evaluation with emerging neural network architectures like spiking neural networks could further enhance tracking capabilities, particularly in scenarios that require real-time, resource-efficient processing.

In summary, this paper lays important groundwork for reliable object tracking through synergistic sensor fusion, offering significant insights and resources to foster further advancements in the domain.

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