Boosting Detection in Crowd Analysis via Underutilized Output Features

• URL: http://arxiv.org/abs/2308.16187v1
• Date: Wed, 30 Aug 2023 17:59:11 GMT
• Title: Boosting Detection in Crowd Analysis via Underutilized Output Features
• Authors: Shaokai Wu, Fengyu Yang
• Abstract summary: Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds. We argue that the potential of these methods has been underestimated, as they offer crucial information for crowd analysis that is often ignored. We propose Crowd Hat, a plug-and-play module that can be easily integrated with existing detection models.
• Score: 8.319283909091595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Detection-based methods have been viewed unfavorably in crowd analysis due to their poor performance in dense crowds. However, we argue that the potential of these methods has been underestimated, as they offer crucial information for crowd analysis that is often ignored. Specifically, the area size and confidence score of output proposals and bounding boxes provide insight into the scale and density of the crowd. To leverage these underutilized features, we propose Crowd Hat, a plug-and-play module that can be easily integrated with existing detection models. This module uses a mixed 2D-1D compression technique to refine the output features and obtain the spatial and numerical distribution of crowd-specific information. Based on these features, we further propose region-adaptive NMS thresholds and a decouple-then-align paradigm that address the major limitations of detection-based methods. Our extensive evaluations on various crowd analysis tasks, including crowd counting, localization, and detection, demonstrate the effectiveness of utilizing output features and the potential of detection-based methods in crowd analysis.

Related papers

• Generative Edge Detection with Stable Diffusion [52.870631376660924]
  Edge detection is typically viewed as a pixel-level classification problem mainly addressed by discriminative methods. We propose a novel approach, named Generative Edge Detector (GED), by fully utilizing the potential of the pre-trained stable diffusion model. We conduct extensive experiments on multiple datasets and achieve competitive performance.
  arXiv  Detail & Related papers  (2024-10-04T01:52:23Z)
• LLMCount: Enhancing Stationary mmWave Detection with Multimodal-LLM [1.8326853076179552]
  We introduce LLMCount, the first system to harness the capabilities of large-language models (LLMs) to enhance crowd detection performance. To assess the system's performance, comprehensive evaluations are conducted under diversified scenarios like hall, meeting room, and cinema.
  arXiv  Detail & Related papers  (2024-09-24T16:09:29Z)
• Robust Zero-Shot Crowd Counting and Localization With Adaptive Resolution SAM [55.93697196726016]
  We propose a simple yet effective crowd counting method by utilizing the Segment-Everything-Everywhere Model (SEEM) We show that SEEM's performance in dense crowd scenes is limited, primarily due to the omission of many persons in high-density areas. Our proposed method achieves the best unsupervised performance in crowd counting, while also being comparable to some supervised methods.
  arXiv  Detail & Related papers  (2024-02-27T13:55:17Z)
• Multi-View Knowledge Distillation from Crowd Annotations for Out-of-Domain Generalization [53.24606510691877]
  We propose new methods for acquiring soft-labels from crowd-annotations by aggregating the distributions produced by existing methods. We demonstrate that these aggregation methods lead to the most consistent performance across four NLP tasks on out-of-domain test sets.
  arXiv  Detail & Related papers  (2022-12-19T12:40:18Z)
• LDC-Net: A Unified Framework for Localization, Detection and Counting in Dense Crowds [103.8635206945196]
  The rapid development in visual crowd analysis shows a trend to count people by positioning or even detecting, rather than simply summing a density map. Some recent work on crowd localization and detection has two limitations: 1) The typical detection methods can not handle the dense crowds and a large variation in scale; 2) The density map methods suffer from performance deficiency in position and box prediction, especially in high density or large-size crowds.
  arXiv  Detail & Related papers  (2021-10-10T07:55:44Z)
• Understanding Failures of Deep Networks via Robust Feature Extraction [44.204907883776045]
  We introduce and study a method aimed at characterizing and explaining failures by identifying visual attributes whose presence or absence results in poor performance. We leverage the representation of a separate robust model to extract interpretable features and then harness these features to identify failure modes.
  arXiv  Detail & Related papers  (2020-12-03T08:33:29Z)
• Video Anomaly Detection by Estimating Likelihood of Representations [21.879366166261228]
  Video anomaly is a challenging task because it involves solving many sub-tasks such as motion representation, object localization and action recognition. Traditionally, solutions to this task have focused on the mapping between video frames and their low-dimensional features, while ignoring the spatial connections of those features. Recent solutions focus on analyzing these spatial connections by using hard clustering techniques, such as K-Means, or applying neural networks to map latent features to a general understanding. In order to solve video anomaly in the latent feature space, we propose a deep probabilistic model to transfer this task into a density estimation problem.
  arXiv  Detail & Related papers  (2020-12-02T19:16:22Z)
• From Handcrafted to Deep Features for Pedestrian Detection: A Survey [148.35460817092908]
  Pedestrian detection is an important but challenging problem in computer vision. Over the past decade, significant improvement has been witnessed with the help of handcrafted features and deep features. In addition to single-spectral pedestrian detection, we also review multi-spectral pedestrian detection.
  arXiv  Detail & Related papers  (2020-10-01T14:51:10Z)
• Semi-Supervised Crowd Counting via Self-Training on Surrogate Tasks [50.78037828213118]
  This paper tackles the semi-supervised crowd counting problem from the perspective of feature learning. We propose a novel semi-supervised crowd counting method which is built upon two innovative components.
  arXiv  Detail & Related papers  (2020-07-07T05:30:53Z)

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.