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Related Experiment Video

Updated: Jun 1, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

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Published on: May 7, 2019

Efficient feedforward categorization of objects and human postures with address-event image sensors.

Shoushun Chen¹, Polina Akselrod, Bo Zhao

¹School of Electrical and Electronic Engineering (EEE), Nanyang Technological University, Singapore. eechenss@ntu.edu.sg

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 15, 2011

Summary

This study introduces a novel algorithm for real-time human posture recognition using event-based sensors and bio-inspired vision processing. The system achieves high accuracy with efficient computation, making it suitable for hardware implementation.

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Area of Science:

Computer Vision
Biologically Inspired Computing
Robotics

Background:

Real-time object and human posture categorization presents computational challenges.
Existing bio-inspired methods often require significant hardware resources.

Purpose of the Study:

To propose an efficient algorithm for feedforward categorization of human postures using event-based sensors.
To develop a system combining event-based hardware with bio-inspired software for enhanced performance.

Main Methods:

Utilized an event-based temporal difference image sensor for input video.
Extracted size and position invariant line features inspired by primate visual cortex models.
Employed a modified line segment Hausdorff distance classifier with on-the-fly cluster-based categorization.

Main Results:

Achieved approximately 90% average success rate in human posture categorization.
Required fewer hardware resources compared to state-of-the-art methods.
Reduced computation complexity by at least five times.

Conclusions:

The proposed algorithm offers an efficient and accurate solution for real-time human posture recognition.
The system's reduced complexity and hardware requirements make it ideal for event-based circuit implementation.