DensePed-Lite: Quality-Aware Adaptive Detection for Dense Pedestrians under Occlusion

arXiv:2609.39467 · cs.CV · Submitted 2026-09-30 · Read on arXiv

cs.CV

Submitted: 2026-09-30

Updated: 2026-09-30

Comments: Accepted at WISE 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 14× in this paper · explained in 1 episode
• Ablation — 9× in this paper · explained in 1 episode
• Redundancy — 6× in this paper · explained in 2 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Consistency — 4× in this paper · explained in 3 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Hypergraph — 2× in this paper · explained in 2 episodes
• Adam — 1× in this paper · explained in 2 episodes
• Alignment — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Data Fusion — 1× in this paper · explained in 3 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Multimodal Fusion — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• What is YOLOv5: A deep look into the internal features of the popular object detector
• CrowdHuman: A Benchmark for Detecting Human in a Crowd
• YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
• YOLOv10: Real-Time End-to-End Object Detection

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