People Detection Citra Kerumunan di Masa Pandemi COVID-19 dengan Menggunakan Metode Histogram of Oriented Gradients (HOG)

Di masa pademi COVID-19, pencegahan kerumunan manusia adalah salah satu pencegahan penyebaran virus Corona. Dari permasalahan tersebut dari penelitian ini melakukan deteksi citra manusia di saat terjadi kerumunan manusia. Dari deteksi tersebut digunakan untuk mengetahui seberapa jumlah manusia yang terdeteksi di dalam citra. Untuk melakukan proses deteksi manusia menggunakan metode Histogram of Oriented Gradients (HOG). Metode HOG digunakan sebagai ekstraksi bentuk pada manusia sebagai nilai data traning. Ada beberapa proses ekstraksi HOG yaitu (1) gradient computatation, (2) gradient vote, dan (3) block histogram normalization. Setelah mendapatkan nilai ekstraksi bentuk pada manusia untuk mendapatkan hasil deteksi, dilakukan proses menggunakan metode klasifikasi SVM. Hasil yang baik didapat saat melakukan pengujian pada citra kerumunan, yaitu terdeteksi manusia pada posisi berkerumun

People Detection and Pose Classification Inside a Moving Train Using Computer Vision

This paper has been presented at : 5th International Visual Informatics Conference (IVIC 2017)Also part of the Image Processing, Computer Vision, Pattern Recognition, and Graphics book sub series (LNIP, volume 10645)The use of surveillance video cameras in public transport is increasingly regarded as a solution to control vandalism and emergency situations. The widespread use of cameras brings in the problem of managing high volumes of data, resulting in pressure on people and resources. We illustrate a possible step to automate the monitoring task in the context of a moving train (where popular background removal algorithms will struggle with rapidly changing illumination). We looked at the detection of people in three possible postures: Sat down (on a train seat), Standing and Sitting (half way between sat down and standing). We then use the popular Histogram of Oriented Gradients (HOG) descriptor to train Support Vector Machines to detect people in any of the predefined postures. As a case study, we use the public BOSS dataset. We show different ways of training and combining the classifiers obtaining a sensitivity performance improvement of about 12% when using a combination of three SVM classifiers instead of a global (all classes) classifier, at the expense of an increase of 6% in false positive rate. We believe this is the first set of public results on people detection using the BOSS dataset so that future researchers can use our results as a baseline to improve upon.The work described here was carried out as part of the OBSERVE project funded by the Fondecyt Regular Program of Conicyt (Chilean Research Council for Science and Technology) under grant no. 1140209. S.A. Velastin is grateful to funding received from the Universidad Carlos III de Madrid, the European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreement no. 600371, el Ministerio de Economía y Competitividad (COFUND2013-51509) and Banco Santander

PASSENGER DETECTION THOUGH VIDEO PROCESSING AND SIGNAL SENSORS IN THE BOSTON SUBWAY TO ADDRESS LEFT-BEHIND PASSENGERS

Crowding is one the most common problems for public transportation systems worldwide. It has been proven to cause anxiety to commuters and create reliability problems when commuters are not able to board on the first train or bus that arrives. These commuters are referred as left-behind passengers, and their number is directly related to various basic performance measures of public transportation systems that represent the user’s experience. Among these measures the most significant are ridership, service quality and, more importantly, travel time. Identifying left behind passengers is a tool to address crowding in stations and respond appropriately, by applying various operational strategies such as decreasing headways. The methodology proposed in this study has been applied to two stations with high probability of left behind passengers, Sullivan Square and North Station on the MBTA Orange Line in Boston, Massachusetts. Two types of technologies were used to detect passengers being left behind in the platform. The first one was an object detection software, namely You Only Look Once (YOLO), using surveillance cameras. The second type was a Bluetooth and Wi-Fi sensor mounted on the two selected stations. Moreover, manual counts of left behind passengers were collected in the two stations. Both technologies will be individually compared with the manual counts to test accuracy and precision. Finally, the two technologies are compared with the manual counts to determine a best way to detect left behind passengers

Measuring Left-Behinds on Subway

ISA# 92312Publication Date from Cover: May 2018The Massachusetts Bay Transportation Authority (MBTA) uses performance measures to monitor its service and measure improvement. This project supports the development of measures that track the customer experience instead of the performance of vehicles. Current measures are based on fare card records and assume that passengers are able to get on the first available vehicle that arrives at a stop or station. There is not currently a way to measure people left behind on subway platforms when vehicles are too full to board. This report presents the development of methods to measure or estimate the number of passengers that are left behind when vehicles are too crowded to board and the distribution of waiting times experienced by passengers, accounting for left-behind passengers. In addition to making use of existing vehicle location data, the study includes evaluation of two potential technologies for measuring passengers: automated passenger counting from surveillance video feeds, and tracking of Media Access Control (MAC) addresses from Bluetooth and Wi-Fi-enabled wireless devices. The occurrence of at least one passenger being left behind can be estimated with 90% accuracy, and the total number left-behind passengers during a rush period can be estimated within 10%. Challenges and opportunities for the future are identified

Pattern Recognition Letters People silhouette extraction from people detection bounding boxes in images

International audienceIn many applications such as video surveillance or autonomous vehicles, people detection is a key element, often based on feature extraction and combined with supervised classification. Usually, output of these methods is in the form of a bounding-box containing an extracted people along with the background. But in specific application contexts, this bounding box information is not sufficient and a precise segmentation of people silhouette is needed inside the bounding box. For videos, this is actually solved by using background subtraction strategies. However, this cannot be considered for the case of still images that also occur in many video surveillance applications. To that aim, we propose to consider that issue in this paper. The principle is to devise a complete scheme for people segmentation inside people detection bounding boxes. Such a scheme relies on several steps: pre-processing, feature extraction and probability map computation to approximately locate people silhouette, and graph cut clustering to refine the silhouette from the map prior. Since many different methods can be considered, along with their associated parameters, tuning, we use a systematic approach towards determining the best combination scheme to conceive a segmentation scheme. The F-measure is used as a benchmark for evaluation. Experimental results show the benefit of the proposed approach that goes beyond the actual state-of-the-art