# Temporal Early Exits for Efficient Video Object Detection

Source: [Temporal Early Exits for Efficient Video Object Detection](<https://www.sota2.com/research/paper/68fa6a3e1e209131cea26f27>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:20:37.043Z

Authors: Amin Sabet, Jonathon Hare, Bashir Al-Hashimi, Geoff V. Merrett

Published: 2021-06-21T15:49:46.000Z

## Abstract

Transferring image-based object detectors to the domain of video remains challenging under resource constraints. Previous efforts utilised optical flow to allow unchanged features to be propagated, however, the overhead is considerable when working with very slowly changing scenes from applications such as surveillance. In this paper, we propose temporal early exits to reduce the computational complexity of per-frame video object detection. Multiple temporal early exit modules with low computational overhead are inserted at early layers of the backbone network to identify the semantic differences between consecutive frames. Full computation is only required if the frame is identified as having a semantic change to previous frames; otherwise, detection results from previous frames are reused. Experiments on CDnet show that our method significantly reduces the computational complexity and execution of per-frame video object detection up to $34 \\times$ compared to existing methods with an acceptable reduction of 2.2\\% in mAP.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2106.11208>)

[PDF](<https://arxiv.org/pdf/2106.11208.pdf>)