The WILDTRACK Multi-Camera Person Dataset
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Summary
A large-scale HD dataset named WILDTRACK is provided which finally makes advanced deep learning methods applicable to people detection problems and benchmarking multi-camera state of the art detectors on this new dataset.
- Type
- preprint
- Published
- 2017-07-28
- Cited by
- 23
- References
- 30
- Access
- Open access
- OpenAlex
- https://openalex.org/W2741213766
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:34605644
Keywords
Computer science, Computer vision, Artificial intelligence
References
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- SALSA: A Novel Dataset for Multimodal Group Behavior Analysis
- Distributed video acquisition and annotation for sport-event summarization
- Robust multiple cameras pedestrian detection with multi-view Bayesian network
- SCOOP: A Real-Time Sparsity Driven People Localization Algorithm
- Pedestrian Detection: An Evaluation of the State of the Art
- Image analysis for video surveillance based on spatial regularization of a statistical model-based change detection
- Pedestrian detection: A benchmark
- Multi-cue onboard pedestrian detection
- Monocular Pedestrian Detection: Survey and Experiments
- Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol
- Are we ready for autonomous driving? The KITTI vision benchmark suite
- A mobile vision system for robust multi-person tracking
- Multicamera People Tracking with a Probabilistic Occupancy Map
- Histograms of oriented gradients for human detection
- Multi-view People Tracking via Hierarchical Trajectory Composition
- Deep Multi-camera People Detection
- Performance Measures and a Data Set for Multi-target, Multi-camera Tracking
- Multiple object tracking using flow linear programming
- A Bayesian computer vision system for modeling human interactions
Cited by
- Deep Occlusion Reasoning for Multi-camera Multi-target Detection
- Mean-Field methods for Structured Deep-Learning in Computer Vision
- Eliminating Exposure Bias and Loss-Evaluation Mismatch in Multiple Object Tracking
- Training Algorithms for Multiple Object Tracking
- Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking
- The MTA Dataset for Multi Target Multi Camera Pedestrian Tracking by Weighted Distance Aggregation
- Tracking Pedestrian Heads in Dense Crowd
- Depth Maps Comparisons from Monocular Images by MiDaS Convolutional Neural Networks and Dense Prediction Transformers
- Two-level Data Augmentation for Calibrated Multi-view Detection
- Towards A Framework for Privacy-Preserving Pedestrian Analysis
- Multi-view Tracking Using Weakly Supervised Human Motion Prediction
- Multi-View Domain Adaptive Object Detection on Camera Networks
- MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark
- Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs
- ATLASFusion: Aggregation Tracking with Location-Aware Sparse Fusion for Robust Spatio-Temporal Multi-View Pedestrian Tracking
- PEDRA: Evaluating the Realism of Pedestrian Dynamics in Video Generation
- Multicamera multitarget tracking based on lightweight neural network
- Occlusion-Resistant Object Detection for Dual-View Airborne Platforms
- One Graph to Track Them All: Dynamic GNNs for Single- and Multi-View Tracking
- DeepFly3D: A deep learning-based approach for 3D limb and appendage tracking in tethered, adult Drosophila
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