Crowd density estimation in still images using multiple local features and boosting regression ensemble
- PDF / 1,079,693 Bytes
- 10 Pages / 595.276 x 790.866 pts Page_size
- 25 Downloads / 208 Views
(0123456789().,-volV)(0123456789(). ,- volV)
IAPR-MEDPRAI
Crowd density estimation in still images using multiple local features and boosting regression ensemble Muhammad Shahid Saleem1 • Muhammad Jaleed Khan1 • Khurram Khurshid1 • Muhammad Shehzad Hanif2 Received: 3 September 2018 / Accepted: 8 January 2019 Springer-Verlag London Ltd., part of Springer Nature 2019
Abstract Crowd density estimation is a challenging research problem in computer vision and has many applications in commercial and defense sectors. Various crowd density estimation methods have been proposed by researchers in the past, but there is an utmost need for accurate, robust and efficient crowd density estimation techniques for its practical implementation. In this paper, we propose a fine-tuned and computationally economical, ensemble regression-based machine learning model for crowd density estimation. The WorldExpo’10 dataset has been used for experimental analysis and model performance evaluation. We extract variety of features in texture-based features such as gray-level co-occurrence matrix, local binary pattern and histogram of oriented gradients, structure-based features such as perimeter pixel and the orientation of pixels, and segment-based handcrafted features from each patch of the image and use an optimum combination of these features as input to the regression model. To achieve optimized memory utilization and faster speed, principal component analysis is employed to reduce the dimensions of the lengthy feature vector. Extensive experiments on different fronts ranging from the model hyperparameter optimization, features optimization and features selection were conducted, and at each step, we selected the most favorable results as input to the optimized model. The performance of the model is evaluated based on two popular metrics, i.e., mean absolute error and mean squared error. The comparative analysis shows that the proposed system outperforms the former methods tested on the WorldExpo’10 dataset. Keywords Crowd counting Machine learning Texture features Regression ensemble
1 Introduction Crowd estimation has a wide range of applications in the civilian and military sectors. In civilian applications, crowd monitoring, urban development, crowd flow estimation, infrastructure design and design requirements are major fields that can be highly improved by utilizing crowd density estimation in urban and crowded areas. In security or military applications, surveillance is the critical one. The crowd images are usually cluttered, highly occluded and contain a lot of noise. This is especially evident in scenarios of counting people in large stadiums, concerts and & Khurram Khurshid [email protected] 1
WiSP Lab, Department of Electrical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan
2
Department of Electrical and Computer Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia
fast-moving rallies where a number of people are exceedingly non-homogeneous with respect to the spatial
Data Loading...
