ArticleFrontiers in neurorobotics2019
Robust Event-Based Object Tracking Combining Correlation Filter and CNN Representation.
Article in Frontiers in neurorobotics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- EVtracker: An Event-Driven Spatiotemporal Method for Dynamic Object Tracking.Sensors (Basel, Switzerland) · 2022Article
- High-Temporal-Resolution Object Detection and Tracking Using Images and Events.Journal of imaging · 2022Article
- NeuroVI-based new datasets and space attention network for the recognition and falling detection of delivery packages.Frontiers in neurorobotics · 2022Article
- VSLAM method based on object detection in dynamic environments.Frontiers in neurorobotics · 2022Article
- The Language of Vision.Perception · 2021Article
- Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study.Frontiers in human neuroscience · 2020Article
- Perception Understanding Action: Adding Understanding to the Perception Action Cycle With Spiking Segmentation.Frontiers in neurorobotics · 2020Article
- Event-Based Face Detection and Tracking Using the Dynamics of Eye Blinks.Frontiers in neuroscience · 2020Article
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Authors and funding
2 authors.
Funding
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Abstract
Object tracking based on the event-based camera or dynamic vision sensor (DVS) remains a challenging task due to the noise events, rapid change of event-stream shape, chaos of complex background textures, and occlusion. To address the challenges, this paper presents a robust event-stream object tracking method based on correlation filter mechanism and convolutional neural network (CNN) representation. In the proposed method, rate coding is used to encode the event-stream object. Feature representations from hierarchical convolutional layers of a pre-trained CNN are used to represent the appearance of the rate encoded event-stream object. Results prove that the proposed method not only achieves good tracking performance in many complicated scenes with noise events, complex background textures, occlusion, and intersected trajectories, but also is robust to variable scale, variable pose, and non-rigid deformations. In addition, the correlation filter-based method has the advantage of high speed. The proposed approach will promote the potential applications of these event-based vision sensors in autonomous driving, robots and many other high-speed scenes.
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