Asynchronous Tracking-by-Detection on Adaptive Time Surfaces for Event-based Object Tracking

Abstract

Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in a variety of situations, such as fast motion and low illumination scenes. However, most of the event-based object tracking methods are designed for scenarios with untextured objects and uncluttered backgrounds. There are few event-based object tracking methods that support bounding box-based object tracking. The main idea behind this work is to propose an asynchronous Event-based Tracking-by-Detection (ETD) method for generic bounding box-based object tracking. To achieve this goal, we present an Adaptive Time-Surface with Linear Time Decay (ATSLTD) event-to-frame conversion algorithm, which asynchronously and effectively warps the spatio-temporal information of asynchronous retinal events to a sequence of ATSLTD frames with clear object contours. We feed the sequence of ATSLTD frames to the proposed ETD method to perform accurate and efficient object tracking, which leverages the high temporal resolution property of event cameras. We compare the proposed ETD method with seven popular object tracking methods, that are based on conventional cameras or event cameras, and two variants of ETD. The experimental results show the superiority of the proposed ETD method in handling various challenging environments.

9 pages, 5 figures

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Cited by in corpus (7)

• Asynchronous Blob Tracker for Event Cameras
• VisEvent: Reliable Object Tracking via Collaboration of Frame and Event Flows
• High-temporal-resolution event-based vehicle detection and tracking
• Data-Driven Feature Tracking for Event Cameras With and Without Frames
• SiamEvent: Event-based Object Tracking via Edge-aware Similarity Learning with Siamese Networks
• Event Data Association via Robust Model Fitting for Event-based Object Tracking
• Multi-domain Collaborative Feature Representation for Robust Visual Object Tracking