International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 06 | June 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 2092 Estimation of Crowd Count in a Heavily Occulated Regions Swathi D G 1 , Jalaja G 2 1 Student, Department of Computer Science and Engineering, BNMIT, Karnataka, India 2 Associate Professor, Department of Computer Science and Engineering, BNMIT, Karnataka, India ---------------------------------------------------------------------***---------------------------------------------------------------------- Abstract - Crowd estimation is a challenging task of accurately estimating number of people in a crowd region. This paper aims to address crowd counting problem from the perspective of two models i.e, body part map and structural density map. The two models are created by combining the information of pedestrian, their head and context structure. Deep Convolutional neural networks and motion detection method is used to count the number of people in the crowd region, based on the pixel movement of the video frames. CNN technique improves the efficiency of counting people in videos and high accuracy is achieved. Key Words: Crowd Counting, Deep Convolutional Neural Networks, Motion Detection, Pedestrian Detection, Crowd Estimation. 1.INTRODUCTION Crowd estimation is the task of efficiently estimating number of pedestrians in a dense region. Crowd counting has harassed much curiosity from scientist due to the practical stipulation like for controlling large number of pedestrians and public security. Detection of a human is a basic issue in video supervision systems. It is estimated that the world population will be 11.2 billion in 2100 years, which is double the current population of the world (7.4 billon, 2016). Due to rapidly growing population across the world, crowd analysis and crowd monitoring has become an important field for research. Manually counting people in the dense crowded areas user cannot estimate the accurate crowd count of the pedestrians present in the area. To overcome this, a system is developed to provide crowd count. Crowd count is any dense scene is provided based on three key factors: pedestrian, head and context structure, are planned as two scene models. The first model is body-parts map, which is obtained by finding the body parts of individual person in dense scene and merging the segmentation mask. The second model is structural-density map, which is created based on shape of individual persons obtained from body- parts map. Then result of two models are combined to provide crowd count of the dense scene. There are several applications of crowd counting some of them are listed below: -  Safety monitoring: - Video surveillance camera used in public place for the safety and security of the people may break down due to limitation in the algorithm design of the system. In such scenarios, crowd counting system can used for event detection, congestion control and behavioral analysis.  Intelligence gathering and analysis: - In malls and airport, depending on the number of people entering or length of queue the counters can be set up so that no human resource is wasted.  Designing a public place: - Crowd counting system can be used to design public space like mall, stadium, rail tracks etc. 2. RELATED WORK Cross scene crowd estimation is a difficult task, where no arduous data notations are required for estimating people count of dense crowd scene. Deep convolutional neural network (CNN) classifier is pre-trained to provide crowd count of the dense scene-based crowd density. A new dataset including 108 crowd images with 200000 head notations was introduced to better evaluate accuracy of cross-scene crowd estimation methods. To evaluate the efficiency and reliability of the method experiment was held on already existing datasets i.e, UCSD, UCF_CC_50 and WorldExpo’10 dataset. Cross-scene system fails to provide accurate count of the dense crowd scene [1]. Pedestrian analysis is challenging due to the gesture variation, obstruction, appearance and background clutters. Deep Decompositional network (DNN) classifier was used for parsing crowded images into different human parts such as face, hairs, hands, legs and body. Deep decompositional network together estimates obstructed regions and body parts of person by arranging three hidden layers: obstruction estimation layers, completion layers and decompositional layers. Pedestrian parsing method by DNN provides better accuracy than state- of-art method on crowded images with or without obstruction. The experiment was conducted on large benchmark PPSS dataset for evaluating the efficiency and reliability of pedestrian parsing method by DNN. The DNN system fails to work efficiently in heavy crowded scene [2]. Global regression methods are used for mapping low level features (texture, edge information and segmentation mask) of humans to provide crowd count of the dense scene. The system is evaluated over USCD dataset. The system ignores the spatial information and body structure information of pedestrian, thus fails to provide accurate crowd count of crowded scene [3]. The head is the most visible part from any crowded scene. The head detection is based on advance method of boosted essential features. To reduce a search region a novel point estimator base on gradient adjustment features to identify region similar to the head region from