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Detecting objects in a video is a compute-intensive task. In this paper wepropose CaTDet, a system to speedup object detection by leveraging the temporalcorrelation in video. CaTDet consists of two DNN models that form a cascadeddetector, and an additional tracker to predict regions of interests based onhistoric detections. We also propose a new metric, mean Delay(mD), which isdesigned for latency-critical video applications. Experiments on the KITTIdataset show that CaTDet reduces operation count by 5.1-8.7x with the same meanAverage Precision(mAP) as the single-model Faster R-CNN detector and incursadditional delay of 0.3 frame. On CityPersons dataset, CaTDet achieves 13.0xreduction in operations with 0.8% mAP loss.

CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video