Enhancing the Fairness and Performance of Edge Cameras with Explainable AI
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Abstract (arXiv)
The rising use of Artificial Intelligence (AI) in human detection on Edge camera systems has led to accurate but complex models, challenging to interpret and debug. Our research presents a diagnostic method using Explainable AI (XAI) for model debugging, with expert-driven problem identification and solution creation. Validated on the Bytetrack model in a real-world office Edge network, we found the training dataset as the main bias source and suggested model augmentation as a solution. Our approach helps identify model biases, essential for achieving fair and trustworthy models.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 12 of its 18 references that are in the map. First posted on arXiv 18 Jan 2024.
Builds on
12 of 18 references.
References · 12
- 2017Understanding Black-box Predictions via Influence Functionscited by 1,741
- 2021YOLOX: Exceeding YOLO Series in 2021cited by 1,071
- 2021ByteTrack: Multi-Object Tracking by Associating Every Detection Boxcited by 823
- 2016MOT16: A Benchmark for Multi-Object Trackingcited by 532
- 2018CrowdHuman: A Benchmark for Detecting Human in a Crowdcited by 284
- 2017CityPersons: A Diverse Dataset for Pedestrian Detectioncited by 172
- 2020Adversarial Machine Learning -- Industry Perspectivescited by 122
- 2020Black-box Explanation of Object Detectors via Saliency Mapscited by 76
- 2023Towards Better Explanations for Object Detectioncited by 14
- 2023Towards Trust of Explainable AI in Thyroid Nodule Diagnosiscited by 10
- 2023G-CAME: Gaussian-Class Activation Mapping Explainer for Object Detectorscited by 7
- 2020Auditing and Debugging Deep Learning Models via Decision Boundaries: Individual-level and Group-level Analysiscited by 2
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