Density-aware person detection and tracking in crowds

By Mikel D. Rodriguez, Ivan Laptev, Josef Sivic, Jean-Yves Audibert. 2011

378 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Learning Non-maximum Suppression (2017, background): “[22] bases suppression decisions on estimated crowd density.”
Incorporating Side Information by Adaptive Convolution (2017, background): “The spatial distribution of the objects is preserved in the density map, which also makes it useful for detection (Ma et al. 2015; Kang et al. 2018) and tracking (Rodriguez et al. 2011; Ren et al. 2017).”
JHU-CROWD++: Large-Scale Crowd Counting Dataset and A Benchmark Method (2020, background): “Consequently, computer vision-based crowd analytics and surveillance [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17] have received increased interest.”
On pairwise costs for network flow multi-object tracking (2014, neutral): “We run a “head” detector [23] to detect”
CrowdNet: A Deep Convolutional Network for Dense Crowd Counting (2016, methodology): “Mean Absolute Error Learning to Count [12] 493.4 Density-aware Detection [16] 655.7 FHSc [8] 468.0 Cross-Scene Counting [19] 467.0 Proposed 452.5 Table 1: Quantitative results of our approach along with other state-of-the-art methods on UCF CC 50 Dataset.”
Part-based multiple-person tracking with partial occlusion handling (2012, background): “Such impediment in [9] led previous works such as [3] and [18] to rely on only head detection and ignore the rest of the body.”
Towards Perspective-Free Object Counting with Deep Learning (2016, result): “The CCNN approach only improves the results reported in [19, 6].”
A Convnet for Non-maximum Suppression (2015, background): “Similarly Rodriguez et al. (2011) proposed to adapt the NMS threshold using crowd density estimation.”
Composition Loss for Counting, Density Map Estimation and Localization in Dense Crowds (2018, methodology): “Crowd counting is active an area of research with works tackling the three aspects of the problem: counting-by-regression [23], [17], [12], [4], [28], density map estimation [17], [7], [29], [20], [30] and localization [18], [22].”
Single-Image Crowd Counting via Multi-Column Convolutional Neural Network (2016, background): “[26] employs density map estimation to obtain better head detection results in crowd scenes.”
DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation (2017, methodology): “Global detection scores are employed to estimate crowd densities and utilized for object tracking in [26].”
Optimized Pedestrian Detection for Multiple and Occluded People (2013, background): “Recently, several other papers have also explored the application of combinatorial optimization methods to the problem of object detection [8, 2, 18, 6, 21].”
Multi-source Multi-scale Counting in Extremely Dense Crowd Images (2013, methodology): “Figure 9: Analysis of comparison: Bars and lines in red depict [20], green show [13], blue shows the results using proposed approach, while ground truth is shown in black.”
Interactive Object Counting (2014, methodology): “Generating a pixel-level object density estimation not only has the advantage of estimating object counts in any region of the image, but can also be used to boost the confidence of object detectors as shown in [15].”
Generating High-Quality Crowd Density Maps Using Contextual Pyramid CNNs (2017, background): “Various approaches have been proposed to tackle the problem of crowd counting in images [12, 5, 16, 44, 50] and videos [2, 9, 26, 7].”
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (2017, methodology): “Various approaches have been proposed to tackle the problem of crowd counting in images [41, 19, 52, 107, 111] and videos [12, 35, 77, 21].”