Static monitoring camera context information can go far beyond the few seconds that regular video comprehending models can notice: Over several days, subjects might act much the same way, and foundation objects don’t change. Due to power and storage constraints, the use of a motion trigger results in sampling frequencies that are subdued, typically no quicker than one frame per second, and occasionally random. For models to perform effectively in this circumstance, they need to be able to withstand erratic sampling rates. This paper presents a method for improving performance with a novel camera by utilizing temporal context from unlabeled frames. Our model, Context R-CNN, uses an attention-based approach to improve object detection performance on the current frame by aggregating contextual features from other frames and indexing into a per-camera-constructed long-term memory bank. In two scenarios, Context R-CNN is used: vehicle location in rush hour gridlock cameras and species identification with camera traps, showing that Setting R-CNN outflanks solid baselines in the two cases. What’s more, we show that expanding the relevant time skyline further develops results. When applied to camera trap data from the Snapshot Serengeti dataset, Context R-CNN with context from a month of photos outperforms a single-frame baseline by 16.8% mAP and a 3-D convolution-based baseline by 12.3% mAP.