Depth-Guided Video Object Counting in Crowded Scenes

• URL: http://arxiv.org/abs/2608.06236v1
• Date: Thu, 06 Aug 2026 16:24:41 GMT
• Title: Depth-Guided Video Object Counting in Crowded Scenes
• Abstract summary: Our primary objective is to advance video object counting in crowded scenes.<n>Existing methods rely on RGB information, limiting their discriminative ability in crowded and occluded conditions.<n>We propose a Depth-Guided Detector (DG-Det) along with a general post-processing pipeline.
• Score: 43.4833789306685
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Our primary objective is to advance video object counting in crowded scenes, aiming to robustly count all instances of a target category based on given text or visual prompts. Existing methods rely on RGB information, limiting their discriminative ability in crowded and occluded conditions. To address this, we propose a Depth-Guided Detector (DG-Det) along with a general post-processing pipeline. By integrating depth cues with multi-scale RGB-D cross-attention and explicit occlusion prediction, our method enhances spatial understanding and achieves robust detection in crowded and occluded scenes. Furthermore, we introduce a unified de-duplication framework to eliminate cross-frame redundant counting. To facilitate future research, we also release a new RGB-D Video Object Counting dataset featuring depth information and multiple object categories persequence. Extensive experiments demonstrate that our method achieves a 62.01\% reduction in MAE compared to existing baselines, and also produces consistent improvements in RMSE. We provide the source code at https://github.com/streamer-AP/DG-Net and the dataset at https://huggingface.co/datasets/aerospace123/RGBD-VideoCount.

Related papers

• NAIMA: Semantics Aware RGB Guided Depth Super-Resolution [50.15623093332659]
  Guided depth super-resolution (GDSR) is a multi-modal approach for depth map super-resolution that relies on a low-resolution depth map and a high-resolution RGB image to restore finer structural details.<n>We propose a solution that introduces global contextual semantic priors, generated from pretrained vision transformer token embeddings.
  arXiv  Detail & Related papers  (2026-04-06T04:14:59Z)
• Embodied Crowd Counting [86.10533153162476]
  Embodied Crowd Counting (ECC) is an interactive simulator that enables large scale scenes and large object quantity.<n>A prior probability distribution that approximates realistic crowd distribution is introduced to generate crowds.<n>This method contains a MLLM driven coarse-to-fine navigation mechanism, enabling active Z-axis exploration.
  arXiv  Detail & Related papers  (2025-03-11T12:23:34Z)
• Depth-Assisted Network for Indiscernible Marine Object Counting with Adaptive Motion-Differentiated Feature Encoding [2.3552699229345264]
  Indiscernible marine object counting encounters numerous challenges, including limited visibility in underwater scenes.<n>We have developed a novel dataset comprising 50 videos, from which 800 frames have been extracted and annotated with around 40, point-wise object labels.<n>This dataset accurately represents real underwater environments where indiscernible marine objects are intricately integrated with their surroundings.
  arXiv  Detail & Related papers  (2025-03-11T08:08:04Z)
• Depth Attention for Robust RGB Tracking [21.897255266278275]
  We propose a new framework that leverages monocular depth estimation to counter the challenges of tracking targets that are out of view or affected by motion blur in RGB video sequences. To the best of our knowledge, we are the first to propose a depth attention mechanism and to formulate a simple framework that allows seamlessly integration of depth information with state of the art tracking algorithms.
  arXiv  Detail & Related papers  (2024-10-27T09:47:47Z)
• Depth-Guided Semi-Supervised Instance Segmentation [62.80063539262021]
  Semi-Supervised Instance (SSIS) aims to leverage an amount of unlabeled data during training. Previous frameworks primarily utilized the RGB information of unlabeled images to generate pseudo-labels. We introduce a Depth-Guided (DG) framework to overcome this limitation.
  arXiv  Detail & Related papers  (2024-06-25T09:36:50Z)
• ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection [51.16181295385818]
  We first collect an annotated RGB-D video SODOD (DSOD-100) dataset, which contains 100 videos within a total of 9,362 frames. All the frames in each video are manually annotated to a high-quality saliency annotation. We propose a new baseline model, named attentive triple-fusion network (ATF-Net) for RGB-D salient object detection.
  arXiv  Detail & Related papers  (2024-06-18T12:09:43Z)
• ViDaS Video Depth-aware Saliency Network [40.08270905030302]
  We introduce ViDaS, a two-stream, fully convolutional Video, Depth-Aware Saliency network. It addresses the problem of attention modeling in-the-wild" via saliency prediction in videos. Network consists of two visual streams, one for the RGB frames, and one for the depth frames. It is trained end-to-end and is evaluated in a variety of different databases with eye-tracking data.
  arXiv  Detail & Related papers  (2023-05-19T15:04:49Z)
• Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization [20.46053083071752]
  We propose and benchmark a new dataset, Localized Visual DeepFake (LAV-DF) LAV-DF consists of strategic content-driven audio, visual and audio-visual manipulations. The proposed baseline method, Boundary Aware Temporal Forgery Detection (BA-TFD), is a 3D Convolutional Neural Network-based architecture.
  arXiv  Detail & Related papers  (2023-05-03T08:48:45Z)
• Is Depth Really Necessary for Salient Object Detection? [50.10888549190576]
  We make the first attempt in realizing an unified depth-aware framework with only RGB information as input for inference. Not only surpasses the state-of-the-art performances on five public RGB SOD benchmarks, but also surpasses the RGBD-based methods on five benchmarks by a large margin.
  arXiv  Detail & Related papers  (2020-05-30T13:40:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.