Keypoint-based object tracking and localization using networks of low-power embedded smart cameras
This work addresses the problem of enabling real-time object tracking on resource-constrained devices, which is incremental as it optimizes existing keypoint methods for low-power systems.
The paper tackles the challenge of object tracking and localization with low-power embedded cameras by proposing a multi-view binary keypoints descriptor approach, achieving a compromise between processing power, accuracy, and bandwidth, as tested on distributed smart cameras.
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.