Beyond Counting: Comparisons of Density Maps for Crowd Analysis Tasks-Counting, Detection, and Tracking

• Published in: IEEE transactions on circuits and systems for video technology Vol. 29; no. 5; pp. 1408 - 1422
• Main Authors: Kang, Di, Ma, Zheng, Chan, Antoni B.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Computer vision; Convolution; Convolutional neural networks; Counting; crowd counting; crowd density map estimation; Density; detection; Estimation; Feature extraction; Forestry; Image resolution; Localization; Measurement; Object recognition; Production methods; Regression analysis; Spatial data; Task analysis; Tracking; Videos

For crowded scenes, the accuracy of object-based computer vision methods declines when the images are low-resolution and objects have severe occlusions. Taking counting methods for example, almost all the recent state-of-the-art counting methods bypass explicit detection and adopt regression-based m...