Keypoint-based object tracking and localization using networks of low-power embedded smart cameras

TL;DR

This paper introduces a real-time object tracking and localization method using multi-view binary keypoints for low-power embedded cameras, balancing accuracy, processing power, and bandwidth.

Contribution

It proposes a novel multi-view binary keypoints descriptor and optimization techniques tailored for low-power embedded systems, enabling effective distributed object tracking.

Findings

Effective real-time tracking with low-power cameras

Balanced accuracy and bandwidth consumption

Enhanced keypoints descriptor performance

Abstract

Object tracking and localization is a complex task that typically requires processing power beyond the capabilities of low-power embedded cameras. This paper presents a new approach to real-time object tracking and localization using multi-view binary keypoints descriptor. The proposed approach offers a compromise between processing power, accuracy and networking bandwidth and has been tested using multiple distributed low-power smart cameras. Additionally, multiple optimization techniques are presented to improve the performance of the keypoints descriptor for low-power embedded systems.