CSID: Center, Scale, Identity and Density-aware Pedestrian Detection in a Crowd
Explore this paper's citation graph
Summary
The proposed CSID pedestrian detector is evaluated using the novel ID-NMS technique, and new state-of-the-art results on two benchmark data sets (CityPersons and CrowdHuman) for pedestrian detection are achieved.
- Type
- article
- Published
- 2019-10-21
- Cited by
- 13
- References
- 32
- OpenAlex
- https://openalex.org/W2980389655
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:204801146
Keywords
Pedestrian detection, Pedestrian, Computer science, Artificial intelligence, Benchmark (surveying)
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Pedestrian detection aided by deep learning semantic tasks
- A discriminative deep model for pedestrian detection with occlusion handling
- Optimized Pedestrian Detection for Multiple and Occluded People
- Fast Feature Pyramids for Object Detection
- Object Detection with Discriminatively Trained Part Based Models
- YOLO9000: Better, Faster, Stronger
- CityPersons: A Diverse Dataset for Pedestrian Detection
- Learning Non-maximum Suppression
- What Can Help Pedestrian Detection?
- Deep Layer Aggregation
- Soft-NMS — Improving Object Detection with One Line of Code
- Improving Object Localization with Fitness NMS and Bounded IoU Loss
- Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection
- CrowdHuman: A Benchmark for Detecting Human in a Crowd
- CornerNet: Detecting Objects as Paired Keypoints
- High-Level Semantic Feature Detection: A New Perspective for Pedestrian Detection
- Objects as Points
- Acquisition of Localization Confidence for Accurate Object Detection
- Iterative Crowd Counting
Cited by
- Adapted Center and Scale Prediction: More Stable and More Accurate
- OPEDD: Offroad Pedestrian Detection Dataset
- Detection of panoramic vision pedestrian based on deep learning
- A Unified Multi-Task Learning Architecture for Fast and Accurate Pedestrian Detection
- Dynamic Dual-Peak Network: A real-time human detection network in crowded scenes
- Multi-dimensional weighted cross-attention network in crowded scenes
- Design Guidelines on Deep Learning–based Pedestrian Detection Methods for Supporting Autonomous Vehicles
- SAL-Net: Self-Supervised Attribute Learning for Object Recognition and Segmentation
- Human-Cascaded network for Robust Detection of Occluded Pedestrian
- Selective Kernel and Spatial Grouping Attention Network for Occluded Pedestrian Detection
- F2DNet: Fast Focal Detection Network for Pedestrian Detection
- Safe object detection in AMRs - a Survey
- CGMA: An improved multi-attribute CIoU-guided enabled pedestrian detection
Related papers
- Attribute-Aware Pedestrian Detection in a Crowd
- Learning to Recognize Pedestrian Attribute
- An Efficient Pedestrian Detection Method Based on YOLOv2
- Graininess-Aware Deep Feature Learning for Robust Pedestrian Detection
- Resisting the Distracting-factors in Pedestrian Detection
- Part-guided Network for Pedestrian Attribute Recognition
- HeadNet: Pedestrian Head Detection Utilizing Body in Context