# Spatially Supervised Recurrent Convolutional Neural Networks for Visual Object Tracking

arXiv: https://arxiv.org/abs/1607.05781 · HTML: https://mlsanity.com/paper/1607.05781/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2016.

## Built on by (most cited first, 13 of 13)

- [MFGNet: Dynamic Modality-Aware Filter Generation for RGB-T Tracking](https://mlsanity.com/paper/2107.10433/) (2021)
- [Mobile Augmented Reality: User Interfaces, Frameworks, and Intelligence](https://mlsanity.com/paper/2106.08710/) (2021)
- [BronchoPose: an analysis of data and model configuration for vision-based bronchoscopy pose estimation](https://mlsanity.com/paper/2204.11982/) (2022)
- [TDIOT: Target-driven Inference for Deep Video Object Tracking](https://mlsanity.com/paper/2103.11017/) (2021)
- [Temp-Frustum Net: 3D Object Detection with Temporal Fusion](https://mlsanity.com/paper/2104.12106/) (2021)
- [Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment](https://mlsanity.com/paper/2408.05940/) (2024)
- [Finding a Needle in a Haystack: Tiny Flying Object Detection in 4K Videos using a Joint Detection-and-Tracking Approach](https://mlsanity.com/paper/2105.08253/) (2021)
- [SGE net: Video object detection with squeezed GRU and information entropy map](https://mlsanity.com/paper/2106.07224/) (2021)
- [A Pedestrian Detection and Tracking Framework for Autonomous Cars: Efficient Fusion of Camera and LiDAR Data](https://mlsanity.com/paper/2108.12375/) (2021)
- [A deep neural network for multi-species fish detection using multiple acoustic cameras](https://mlsanity.com/paper/2109.10664/) (2021)
- [Adaptive Subsampling for ROI-based Visual Tracking: Algorithms and FPGA Implementation](https://mlsanity.com/paper/2112.09775/) (2021)
- [Deep Learning Method for Cell-Wise Object Tracking, Velocity Estimation and Projection of Sensor Data over Time](https://mlsanity.com/paper/2306.06126/) (2023)
- [Layout-Independent License Plate Recognition via Integrated Vision and Language Models](https://mlsanity.com/paper/2510.10533/) (2025)

## Abstract (arXiv)

In this paper, we develop a new approach of spatially supervised recurrent convolutional neural networks for visual object tracking. Our recurrent convolutional network exploits the history of locations as well as the distinctive visual features learned by the deep neural networks. Inspired by recent bounding box regression methods for object detection, we study the regression capability of Long Short-Term Memory (LSTM) in the temporal domain, and propose to concatenate high-level visual features produced by convolutional networks with region information. In contrast to existing deep learning based trackers that use binary classification for region candidates, we use regression for direct prediction of the tracking locations both at the convolutional layer and at the recurrent unit. Our extensive experimental results and performance comparison with state-of-the-art tracking methods on challenging benchmark video tracking datasets shows that our tracker is more accurate and robust while maintaining low computational cost. For most test video sequences, our method achieves the best tracking performance, often outperforms the second best by a large margin.