# CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video

arXiv: https://arxiv.org/abs/1810.00434 · HTML: https://mlsanity.com/paper/1810.00434/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2018.

## Built on by (most cited first, 7 of 7)

- [SALISA: Saliency-based Input Sampling for Efficient Video Object Detection](https://mlsanity.com/paper/2204.02397/) (2022)
- [Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics](https://mlsanity.com/paper/2106.15315/) (2021)
- [Survey: Exploiting Data Redundancy for Optimization of Deep Learning](https://mlsanity.com/paper/2208.13363/) (2022)
- [A novel efficient Multi-view traffic-related object detection framework](https://mlsanity.com/paper/2302.11810/) (2023)
- [PatchNet -- Short-range Template Matching for Efficient Video Processing](https://mlsanity.com/paper/2103.07371/) (2021)
- [MTD: Multi-Timestep Detector for Delayed Streaming Perception](https://mlsanity.com/paper/2309.06742/) (2023)
- [A comprehensive overview of deep learning models for object detection from videos/images](https://mlsanity.com/paper/2601.14677/) (2026)

## Abstract (arXiv)

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