Context R-CNN- Long Term Temporal Context for Per-Camera Object Detection

Explore an in-depth explanation of the Context R-CNN paper, which introduces a novel approach to object detection in static camera environments. Learn how this model leverages long-term temporal context to improve detection accuracy in scenarios with irregularly sampled data, such as wildlife traps and traffic cameras. Discover the architecture's key components, including short-term and long-term memory mechanisms, and understand how attention-based techniques are used to aggregate contextual features from other frames. Examine quantitative and qualitative results demonstrating the model's performance gains over baseline methods, and gain insights into its effectiveness in reducing false positives. Delve into the paper's problem formulation, methodology, and conclusions through a comprehensive breakdown of its contents, including an analysis of static camera data and the model's application to species detection and vehicle detection tasks.

- Intro & Overview
- Problem Formulation
- Static Camera Data
- Architecture Overview
- Short-Term Memory
- Long-Term Memory
- Quantitative Results
- Qualitative Results
- False Positives
- Appendix & Conclusion