# HAVT-IVD: Heterogeneity-Aware Cross-Modal Network for Audio-Visual Surveillance: Idling Vehicles Detection With Multichannel Audio and Multiscale Visual Cues

arXiv: https://arxiv.org/abs/2504.16102 · HTML: https://mlsanity.com/paper/2504.16102/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 16 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 12 of 12)

- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Audio-Visual Segmentation](https://mlsanity.com/paper/2207.05042/) (2022)
- [AVSegFormer: Audio-Visual Segmentation with Transformer](https://mlsanity.com/paper/2307.01146/) (2023)
- [Self-supervised Moving Vehicle Tracking with Stereo Sound](https://mlsanity.com/paper/1910.11760/) (2019)
- [There is More than Meets the Eye: Self-Supervised Multi-Object Detection and Tracking with Sound by Distilling Multimodal Knowledge](https://mlsanity.com/paper/2103.01353/) (2021)
- [Multimodal Variational Auto-encoder based Audio-Visual Segmentation](https://mlsanity.com/paper/2310.08303/) (2023)
- [Improving Audio-Visual Segmentation with Bidirectional Generation](https://mlsanity.com/paper/2308.08288/) (2023)
- [Semantic Object Prediction and Spatial Sound Super-Resolution with Binaural Sounds](https://mlsanity.com/paper/2003.04210/) (2020)
- [LoCoNet: Long-Short Context Network for Active Speaker Detection](https://mlsanity.com/paper/2301.08237/) (2023)
- [Real-Time Idling Vehicles Detection using Combined Audio-Visual Deep Learning](https://mlsanity.com/paper/2305.14579/) (2023)
- [Joint Audio-Visual Idling Vehicle Detection with Streamlined Input Dependencies](https://mlsanity.com/paper/2410.21170/) (2024)
- [Remote Detection of Idling Cars Using Infrared Imaging and Deep Networks](https://mlsanity.com/paper/1804.10805/) (2018)

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

- [Towards Accurate and Robust Surveillance Roadside IVD via Trackletized Audio-Visual Reasoning](https://mlsanity.com/paper/2606.22299/) (2026)

## Abstract (arXiv)

Idling vehicle detection (IVD) uses surveillance video and multichannel audio to localize and classify vehicles in the last frame as moving, idling, or engine-off in pick-up zones. IVD faces three challenges: (i) modality heterogeneity between visual cues and audio patterns; (ii) large box scale variation requiring multi-resolution detection; and (iii) training instability due to coupled detection heads. The previous end-to-end (E2E) model with simple CBAM-based bi-modal attention fails to handle these issues and often misses vehicles. We propose HAVT-IVD, a heterogeneity-aware network with a visual feature pyramid and decoupled heads. Experiments show HAVT-IVD improves mAP by 7.66 over the disjoint baseline and 9.42 over the E2E baseline.