A texture based mani-fold approach for crowd density estimation using Gaussian Markov Random Field

The objective of this work is to leverage the clues obtained from mani-fold sources, to figure out the density of people existent in exceptionally dense crowded regions. The complications in crowd density estimation include perspective effect, occlusion, clutter background, textured brick surface, and few pixels per target. In crowd scenarios with these complications, detection based techniques are not accurate, even none of the single feature alone is suitable to estimate crowd density. Therefore, our methodology depends on mani-fold sources such as head detection with low confidence, recurrence of texture elements by using frequency domain, wavelet and scale invariant feature transform (SIFT) descriptor to measure the density count. The information obtained from manifold sources is used to train a support vector machine (SVM), which generates a patch count estimation. Next, a Gaussian-based Markov Random Field (MRF) is applied on image patches to obtain uniformity on crowd count. The Gaussian MRF furnishes the discrepancy in crowd count along with local neighborhoods at multiple scales. We tested our approach on four different datasets such as Shanghai Tech_A, UCF_CC_50, extended UCF_CC_100 and UCSD. The former three datasets are a crisp contrast to existing crowd datasets used in literature which contains almost hundreds or tens of individuals in crowd images. The latter UCSD dataset is used to test the robustness of our technique in low-density crowd too. We compare the proposed method with both traditional and convolutional neural network (CNN) based approaches. Low computational complexity indicates that the proposed technique provides decent performance rate and can be employed in real-world applications. Our experimental results validate the adequacy and efficiency of the intended methodology by measuring the density of crowd images.