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SCTN: Event-based object tracking with energy-efficient deep convolutional spiking neural networks.

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This study introduces the Spiking Convolutional Tracking Network (SCTN), a novel Spiking Neural Network (SNN) for event-based object tracking. SCTN achieves competitive performance with significantly lower energy consumption than traditional methods.

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Area of Science:

  • Neuromorphic Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Event cameras offer asynchronous, event-driven data capture, ideal for tracking moving objects.
  • Spiking Neural Networks (SNNs) excel in event-driven computation and energy efficiency.
  • Existing object tracking methods often lack efficiency and struggle with sparse, temporal data.

Purpose of the Study:

  • To develop a novel Spiking Neural Network (SNN) architecture for event-based object tracking.
  • To improve the utilization of temporal information and sparse event data in tracking.
  • To introduce a new loss function for enhanced SNN training in tracking tasks.

Main Methods:

  • Proposed the Spiking Convolutional Tracking Network (SCTN), a discriminatively trained SNN.
  • Developed a novel exponential Intersection over Union (IoU) loss function in the voltage domain.
  • Introduced DVSOT21, a new dataset for event-based object tracking.

Main Results:

  • SCTN effectively processes segments of events, leveraging temporal associations and sparsity.
  • The proposed loss function enhances tracking accuracy within the SNN framework.
  • Experimental results on DVSOT21 show competitive performance with significantly reduced energy consumption compared to ANN-based trackers.

Conclusions:

  • SCTN represents a pioneering direct SNN training approach for object tracking.
  • The method demonstrates superior energy efficiency, highlighting the potential of neuromorphic hardware for real-time tracking.
  • Event-based tracking with SNNs offers a promising direction for low-power, high-performance visual perception.