Summary of Multi-resolution Rescored Bytetrack For Video Object Detection on Ultra-low-power Embedded Systems, by Luca Bompani et al.
Multi-resolution Rescored ByteTrack for Video Object Detection on Ultra-low-power Embedded Systems
by Luca Bompani, Manuele Rusci, Daniele Palossi, Francesco Conti, Luca Benini
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces Multi-Resolution Rescored Byte-Track (MR2-ByteTrack), a novel video object detection framework designed for ultra-low-power embedded processors. The method reduces the average compute load of an off-the-shelf Deep Neural Network (DNN) based object detector by up to 2.25 times by alternating processing of high-resolution images with multiple down-sized frames. To tackle accuracy degradation due to reduced image input size, MR2-ByteTrack correlates output detections over time using the ByteTrack tracker and corrects potential misclassification using a novel probabilistic Rescore algorithm. The paper demonstrates an average accuracy increase of 2.16% and latency reduction of 43% on the GAP9 microcontroller compared to a baseline frame-by-frame inference scheme. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper introduces a new way to detect objects in videos using tiny computers. Normally, these computers struggle with this task because they need to process big images quickly. The new method, called MR2-ByteTrack, makes it faster and more efficient by splitting the processing into smaller parts. It also uses special algorithms to make sure the object detection is accurate. This new approach works well on tiny computers and can even make them run faster and more accurately than before. |
Keywords
» Artificial intelligence » Inference » Neural network » Object detection
