Counting People With Low-Level Features and Bayesian Regression

By Antoni B. Chan, Nuno Vasconcelos. IEEE Transactions on Image Processing, 2012

401 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/eaac90145829c0209fd6290f883ca1441e26c31c

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Citations in the corpus

Feature Mining for Localised Crowd Counting (2012, methodology): “In practice dotted annotation is no harder than raw count as in the global counting methods [5].”
Cumulative Attribute Space for Age and Crowd Density Estimation (2013, methodology): “Despite the low-level features being very different, the same regression models such as support vector machine regression and Gaussian Processes have been employed for both problems [4, 5, 8, 25].”
Spatiotemporal Group Context for Pedestrian Counting (2014, background): “Obviously, most of the detection-based approaches rely on the training process and most of them are based on a single frame, so they have difficulties to achieve stable results due to the view changes, scale changes, pose changes, and spatial overlapping.”
A Thermal Infrared Video Benchmark for Visual Analysis (2014, methodology): “Fortunately, techniques to count the objects without using an object detector exist [17], [18], [19].”
Leveraging Unlabeled Data for Crowd Counting by Learning to Rank (2018, background): “We focus on crowd counting in still images, but we refer the interested reader to the following papers for examples of crowd counting in video [5, 9, 22].”
Incorporating Side Information by Adaptive Convolution (2017, background): “Previous works have also shown that using side information, e.g., the scene perspective, helps to improve crowd counting accuracy (Chan et al. 2008; Chan and Vasconcelos 2012).”
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation (2017, methodology): “…motivated researchers across various ﬁelds to develop sophisticated methods for crowd analysis and related tasks such as counting [15, 16, 20, 41, 17, 85, 35, 41], density estimation [52, 19, 111, 107, 75, 99, 11], segmentation [46, 27], behaviour analysis [6, 86, 22, 115, 114, 103], tracking…”
CNN-Based cascaded multi-task learning of high-level prior and density estimation for crowd counting (2017, background): “Researchers have attempted to address various aspects of analyzing crowded scenes such as counting [3, 4, 23, 10], density estimation [12, 32, 31, 18, 29, 2], segmentation [11], behavior analysis [24], tracking [21], scene understanding [25] and anomaly detection [19].”
A Posture Recognition-Based Fall Detection System for Monitoring an Elderly Person in a Smart Home Environment (2012, methodology): “However, as we have discussed, multiple moving objects and occlusions are two problems needed to be solved for our fall detection system, which can be addressed by using a multiple cameras scheme with adding corresponding modules for people counting and object classiﬁcation.”