In the year 1999 the world population reached 6 billion, doubling the previous census estimate of 1960. Recently, the United States Census Bureau issued a revised forecast for world population showing a projected growth to 9.4 billion by 2050 (US Census Bureau, http://www.census.gov/ipc/www/worldpop.html). Different research disci- plines have studied the crowd phenomenon and its dynamics from a social, psychological and computational standpoint respectively. This paper presents a survey on crowd analysis methods employed in computer vision research and discusses perspectives from other research disciplines and how they can contribute to the computer vision approach.

Crowd analysis: a survey

Automated vision tracking of project related entities

RGB-D salient object detection (SOD) aims to segment the most attractive objects in a pair of cross-modal RGB and depth images. Currently, most existing RGB-D SOD methods focus on the foreground region when utilizing the depth images. However, the background also provides important information in traditional SOD methods for promising performance. To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task. Specifically, we introduce a Bilateral Attention Module (BAM) with a complementary attention mechanism: foreground-first (FF) attention and background-first (BF) attention. The FF attention focuses on the foreground region with a gradual refinement style, while the BF one recovers potentially useful salient information in the background region. Benefited from the proposed BAM module, our BiANet can capture more meaningful foreground and background cues, and shift more attention to refining the uncertain details between foreground and background regions. Additionally, we extend our BAM by leveraging the multi-scale techniques for better SOD performance. Extensive experiments on six benchmark datasets demonstrate that our BiANet outperforms other state-of-the-art RGB-D SOD methods in terms of objective metrics and subjective visual comparison. Our BiANet can run up to 80 fps on $224\times 224$ RGB-D images, with an NVIDIA GeForce RTX 2080Ti GPU. Comprehensive ablation studies also validate our contributions.

Bilateral Attention Network for RGB-D Salient Object Detection

A survey on player tracking in soccer videos

Most existing RGB-D salient object detection (SOD) methods focus on the foreground region when utilizing the depth images. However, the background also provides important information in traditional SOD methods for promising performance. To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task. Specifically, we introduce a Bilateral Attention Module (BAM) with a complementary attention mechanism: foreground-first (FF) attention and background-first (BF) attention. The FF attention focuses on the foreground region with a gradual refinement style, while the BF one recovers potentially useful salient information in the background region. Benefitted from the proposed BAM module, our BiANet can capture more meaningful foreground and background cues, and shift more attention to refining the uncertain details between foreground and background regions. Additionally, we extend our BAM by leveraging the multi-scale techniques for better SOD performance. Extensive experiments on six benchmark datasets demonstrate that our BiANet outperforms other state-of-the-art RGB-D SOD methods in terms of objective metrics and subjective visual comparison. Our BiANet can run up to 80fps on $224\times224$ RGB-D images, with an NVIDIA GeForce RTX 2080Ti GPU. Comprehensive ablation studies also validate our contributions.

This paper presents a robust approach to non-rigid object tracking in video sequences. The object to track is described by a 2-dimensional point distribution model whose landmarks correspond to interest points that are automatically extracted from the object and described by their geometrical position and their local appearance. The approach is novel in that we describe the appearance locally instead of using the raw texture information. This provides a natural way to robustly handle partial occlusions. A second contribution is that we present a method that allows to learn the model automatically. Our algorithms have been successfully tested on several video streams taken from soccer games and video surveillance footage. They have been implemented with the aim of achieving near real-time performance.

Robust Non-Rigid Object Tracking Using Point Distribution Models.

We present an approach to non-rigid object tracking designed to handle textured objects in crowded scenes captured by non-static cameras. For this purpose, groups of low-level features are combined into a model describing both the shape and the appearance of the object. This results in remarkable robustness to severe partial occlusions, since overlapping objects are unlikely to be indistinguishable in appearance, configuration and velocity all at the same time. The model is learnt incrementally and adapts to varying illumination conditions and target shape and appearance, and is thus applicable to any kind of object. Results on real-world sequences demonstrate the performance of the proposed tracker. The algorithm is implemented with the aim of achieving near real-time performance.

/pdf/robust-non-rigid-object-tracking-using-point-distribution-4sesy73jfx.pdf

Robust non-rigid object tracking using point distribution manifolds

Laser scanning has revolutionized the ability to quantify single-tree morphologies and stand structural variables. In this study, we address the issue of occlusion when scanning a spruce (Picea abies (L.) H.Karst.) and beech (Fagus sylvatica L.) forest with a mobile laser scanner by making use of a unique study site setup. We scanned forest stands (1) from the ground only and (2) from the ground and from above by using a crane. We also examined the occlusion effect by scanning in the summer (leaf-on) and in the winter (leaf-off). Especially at the canopy level of the forest stands, occlusion was very pronounced, and we were able to quantify its impact in more detail. Occlusion was not as noticeable as expected for crown-related variables but, on average, resulted in smaller values for tree height in particular. Between the species, the total tree height underestimation for spruce was more pronounced than that for beech. At the stand level, significant information was lost in the canopy area when scanning from the ground alone. This information shortage is reflected in the relative point counts, the Clark–Evans index and the box dimension. Increasing the voxel size can compensate for this loss of information but comes with the trade-off of losing details in the point clouds. From our analysis, we conclude that the voxelization of point clouds prior to the extraction of stand or tree measurements with a voxel size of at least 20 cm is appropriate to reduce occlusion effects while still providing a high level of detail.

/pdf/what-are-we-missing-occlusion-in-laser-scanning-point-clouds-2tf4at4s.pdf

What Are We Missing? Occlusion in Laser Scanning Point Clouds and Its Impact on the Detection of Single-Tree Morphologies and Stand Structural Variables

Our study considers the development of a reliable tracker for non-rigid objects evolving on cluttered background in crowded scenes captured by moving cameras. For this purpose, we propose an original method that combines two approaches, respectively based on parametric active contours (PAC) and on point distribution model (PDM). The PAC tracker relies on an effective and efficient implementation of contour convergence mechanism to bring a smooth contour to the edges of the target in real-time. The PDM approach collects feature points in the region delineated by the PAC tracker to build and update a model of the target in term of a feature point distribution. Formally, when a novel frame is considered, its feature points are matched with the PDM model. The matching information is used to initialize the novel PAC, whose convergence identify the points that are relevant to update the PDM for the next frame. Hence, the two approaches complement each others. The a priori information provided by the PDM makes the system robust towards occlusions, while the deformation of the PAC increases its robustness towards target appearance changes. Simulations on real-word video sequences demonstrate the performance of our approach.

/pdf/non-rigid-object-tracker-based-on-a-robust-combination-of-24hpoflth4.pdf

Non-rigid object tracker based on a robust combination of parametric active contour and point distribution model

This article presents a modular architecture for multicamera tracking in the context of sports broadcasting. For each video stream, a geometrical module continuously performs the image-to-model homography estimation. A local-feature based tracking module tracks the players in each view. A supervisor module collects, associates and fuses the data provided by the tracking modules. The originality of the proposed system is three-fold. First, it allows to localize the targets on the ground with rotating and zooming cameras; second, it does not use background modeling techniques; and third, the local tracking can cope with severe occlusions. We present experimental results on raw TV-camera footage of a soccer game.

/pdf/a-modular-multi-camera-framework-for-team-sports-tracking-1jt9yyqolx.pdf

A modular multi-camera framework for team sports tracking

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