# TempNet: Temporal Attention Towards the Detection of Animal Behaviour in Videos

arXiv: https://arxiv.org/abs/2211.09950 · HTML: https://mlsanity.com/paper/2211.09950/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Video Classification with Channel-Separated Convolutional Networks](https://mlsanity.com/paper/1904.02811/) (2019)
- [Learning Deep Representations of Appearance and Motion for Anomalous Event Detection](https://mlsanity.com/paper/1510.01553/) (2015)
- [Deep Learning for Detecting Multiple Space-Time Action Tubes in Videos](https://mlsanity.com/paper/1608.01529/) (2016)
- [Efficient Video Classification Using Fewer Frames](https://mlsanity.com/paper/1902.10640/) (2019)
- [L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion](https://mlsanity.com/paper/2005.13736/) (2020)
- [Preservation of High Frequency Content for Deep Learning-Based Medical Image Classification](https://mlsanity.com/paper/2205.03898/) (2022)
- [Movement Tracks for the Automatic Detection of Fish Behavior in Videos](https://mlsanity.com/paper/2011.14070/) (2020)
- [ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos](https://mlsanity.com/paper/1801.02031/) (2018)

## Abstract (arXiv)

Recent advancements in cabled ocean observatories have increased the quality and prevalence of underwater videos; this data enables the extraction of high-level biologically relevant information such as species' behaviours. Despite this increase in capability, most modern methods for the automatic interpretation of underwater videos focus only on the detection and counting organisms. We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos. TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder. TempNet also presents temporal attention during spatial encoding as well as Wavelet Down-Sampling pre-processing to improve model accuracy. Although our system is designed for applications to diverse fish behaviours (i.e, is generic), we demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events. We compare the proposed approach with a state-of-the-art end-to-end video detection method (ReMotENet) and a hybrid method previously offered exclusively for the detection of sablefish's startle events in videos from an existing dataset. Results show that our novel method comfortably outperforms the comparison baselines in multiple metrics, reaching a per-clip accuracy and precision of 80% and 0.81, respectively. This represents a relative improvement of 31% in accuracy and 27% in precision over the compared methods using this dataset. Our computational pipeline is also highly efficient, as it can process each 4-second video clip in only 38ms. Furthermore, since it does not employ features specific to sablefish startle events, our system can be easily extended to other behaviours in future works.