16 accepted papers
Counting is a core capability for multimodal large language models (MLLMs), yet there is no unified counting dataset to rigorously evaluate this ability across image, text, and audio. We present UNICBench, a unified multimodal, multi-level counting benchmark and evaluation toolkit with accurate grou
Crowd counting, an important problem in computer vision, is commonly solved with deep learning approaches, such as convolutional networks and transformers. However, Deep networks often overfit when the available labeled crowd data is scarce. To overcome this, we have designed a pipeline that utilize…
Occlusion is one of the fundamental challenges in crowd counting. In the community, various data-driven approaches have been developed to address this issue, yet their effectiveness is limited. This is mainly because most existing crowd counting datasets on which the methods are trained are based on…
Crowd counting and localization involve extracting the number and distribution of crowds from images or videos using computer vision techniques. Most counting methods are based on density regression and are based on an ``intersection'' hypothesis, *i.e.*, one pixel is influenced by multiple points i…
With the rise of social media, vast amounts of user-uploaded videos (e.g., YouTube) are utilized as training data for Visual Object Tracking (VOT). However, the VOT community has largely overlooked video data-privacy issues, as many private videos have been collected and used for training commercial…
Video Individual Counting (VIC) has received increasing attention for its importance in intelligent video surveillance. Existing works are limited in two aspects, i.e., dataset and method. Previous datasets are captured with fixed or rarely moving cameras with relatively sparse individuals, restrict…
"3D single object tracking (SOT) is an essential task in autonomous driving and robotics. However, learning robust 3D SOT trackers remains challenging due to the limited category-specific point cloud data and the inherent sparsity and incompleteness of LiDAR scans. To tackle these issues, we propose…
"The problem of designing a privacy-preserving camera (PPC) is considered. Previous designs rely on a static point spread function (PSF), optimized to prevent detection of private visual information, such as recognizable facial features. However, the PSF can be easily recovered by measuring the came…
Due to the domain gap between the public large-scale datasets and actual scenes, the crowd counting models trained on the common datasets have a significant performance degradation when applying in practical applications. To address the above issue, one of the solution is to label additional data fr…
This paper investigates crowd counting in the frequency domain, which is a novel direction compared to the traditional view in the spatial domain. By transforming the density map into the frequency domain and using the nice properties of the characteristic function, we propose a novel method that is…
Previous work shows that a better density map representation can improve the performance of crowd counting. In this paper, we investigate learning the density map representation through an unbalanced optimal transport problem, and propose a generalized loss function to learn density maps for crowd c…
In this paper, we propose a progressive unsupervised learning (PUL) framework, which entirely removes the need for annotated training videos in visual tracking. Specifically, we first learn a background discrimination (BD) model that effectively distinguishes an object from background in a contrasti…
The annotation noise in crowd counting is not modeled in traditional crowd counting algorithms based on crowd density maps. In this paper, we first model the annotation noise using a random variable with Gaussian distribution, and derive the pdf of the crowd density value for each spatial location i…
Crowd counting is an important topic in computer vision due to its practical usage in surveillance systems. The typical design of crowd counting algorithms is divided into two steps. First, the ground-truth density maps of crowd images are generated from the ground-truth dot maps (density map genera…
Crowd counting is a challenging task due to factors such as large variations in crowdedness and severe occlusions. Although recent deep learning based counting algorithms have achieved a great progress, the correlation knowledge among samples and the semantic prior have not yet been fully exploited.…
In this paper, a new perspective of congestion is presented to promote the development of traffic video analysis. Our main contributions are threefold: a) An unified and quantifiable definition of congestion is proposed to describe the traffic state in video. b) Based on the definition, a congestion…