Object detection often does not occur in a vacuum. Static cameras, such as wildlife traps, collect lots of irregularly sampled data over a large time frame and often capture repeating or similar events. This model learns to dynamically incorporate other frames taken by the same camera into its object detection pipeline.
OUTLINE:
0:00 – Intro & Overview
1:10 – Problem Formulation
2:10 – Static Camera Data
6:45 – Architecture Overview
10:00 – Short-Term Memory
15:40 – Long-Term Memory
20:10 – Quantitative Results
22:30 – Qualitative Results
30:10 – False Positives
32:50 – Appendix & Conclusion
Paper:
My Video On Attention Is All You Need:
Abstract:
In static monitoring cameras, useful contextual information can stretch far beyond the few seconds typical video understanding models might see: subjects may exhibit similar behavior over multiple days, and background objects remain static. Due to power and storage constraints, sampling frequencies are low, often no faster than one frame per second, and sometimes are irregular due to the use of a motion trigger. In order to perform well in this setting, models must be robust to irregular sampling rates. In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera. Specifically, we propose an attention-based approach that allows our model, Context R-CNN, to index into a long term memory bank constructed on a per-camera basis and aggregate contextual features from other frames to boost object detection performance on the current frame.
We apply Context R-CNN to two settings: (1) species detection using camera traps, and (2) vehicle detection in traffic cameras, showing in both settings that Context R-CNN leads to performance gains over strong baselines. Moreover, we show that increasing the contextual time horizon leads to improved results. When applied to camera trap data from the Snapshot Serengeti dataset, Context R-CNN with context from up to a month of images outperforms a single-frame baseline by 17.9% mAP, and outperforms S3D (a 3d convolution based baseline) by 11.2% mAP.
Authors: Sara Beery, Guanhang Wu, Vivek Rathod, Ronny Votel, Jonathan Huang
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