# Deep Flow Collaborative Network for Online Visual Tracking

arXiv: https://arxiv.org/abs/1911.01786 · HTML: https://mlsanity.com/paper/1911.01786/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [WeClick: Weakly-Supervised Video Semantic Segmentation with Click Annotations](https://mlsanity.com/paper/2107.03088/) (2021)

## Abstract (arXiv)

The deep learning-based visual tracking algorithms such as MDNet achieve high performance leveraging to the feature extraction ability of a deep neural network. However, the tracking efficiency of these trackers is not very high due to the slow feature extraction for each frame in a video. In this paper, we propose an effective tracking algorithm to alleviate the time-consuming problem. Specifically, we design a deep flow collaborative network, which executes the expensive feature network only on sparse keyframes and transfers the feature maps to other frames via optical flow. Moreover, we raise an effective adaptive keyframe scheduling mechanism to select the most appropriate keyframe. We evaluate the proposed approach on large-scale datasets: OTB2013 and OTB2015. The experiment results show that our algorithm achieves considerable speedup and high precision as well.