A crowd-filter for detection of abandoned objects in crowded area

Cho, Chuan-Yu; Tung, Wei-Hao; Wang, Jia-Shung

doi:10.1109/icsenst.2008.4757172

Cited by 4 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An overview of visual systems can be found in [9]. We mention that visual systems also require filters for their data like in the case of [10] where the crowd is filtered out to reveal object left behind such as bags or packets.…”

Section: Related Workmentioning

confidence: 99%

Filters for Wi-Fi Generated Crowd Movement Data

Chilipirea

Petre

Dobre

et al. 2015

2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

View full text Add to dashboard Cite

Cities represent large groups of people that share a common infrastructure, common social groups and/or common interests. With the development of new technologies current cities aim to become what is known as smart cities, in which all the small details of these large constructs are controlled to better improve the quality of life of its inhabitants. One of the important gears that powers a city is given by traffic, be it vehicular or pedestrian. As such traffic is closely related to all other activities that take place inside of a city. Understanding traffic is still a difficult process as we have to be able to not only measure it in the sense of how many people are using a particular path but also in analyzing where people are going and when, while still maintaining individual privacy. And all this has to be done at a scale that would cover most if not all individuals in a city. With the high increase in smartphones adoption we can reliably assume that a large part of the population in cities are carrying with them, at all times, at least one Wi-Fi enabled device. Because Wi-Fi devices are regularly transmitting signals we can rely on these devices to detect individual's movements unobtrusively without identifying or tracking any particular individual. Special sensors that monitor Wi-Fi frequencies can be placed around a city to gather data that can later be used to identify patterns in the traffic flows. We present a set of filters that can be used to minimize the amount of data needed for processing and without negatively impacting the result or the information that can be extracted from this data. Part of the filters we present can be deployed at the sensor level, making the entire system more scalable, while a different part can be executed before data processing thus enabling real time information extraction and a broader temporal and spatial range for data analysis. Some of these filters are particular to Wi-Fi but some of them can be applied to any detection system.

show abstract

Section: Related Workmentioning

confidence: 99%

Filters for Wi-Fi Generated Crowd Movement Data

Chilipirea

Petre

Dobre

et al. 2015

2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

View full text Add to dashboard Cite

show abstract

Autonomous pedestrian detection for crowd surveillance using deep learning framework

Thakur

Nagrath

Jain³

et al. 2023

Soft Comput

View full text Add to dashboard Cite

Pedestrian detection is a key ability required by most computer visions and crowd surveillance applications, with several applications such as person identification, person count and tracking. The number of techniques to identifying pedestrians in images has gradually increased in recent years, even with the significant advances in the state-of-the-art D based framework for object detection model. The research in the field of object detection and image classification has made a stride in the level of accuracy greater than 99% and the level of granularity. A powerful Object Detector, specifically designed for high-end surveillance applications, is needed that will not only position the bounding box and label it, but will also return their relative positions. The size of these bounding boxes can vary depending on the object and how it interacts with the physical world. To address these requirements, an extensive evaluation of the state-of-the-art algorithms has been presented in this paper. The work presented in this paper performs detections on MOT20 dataset using various algorithms and testing on a custom dataset recorded in our organization premises using an Unmanned Aerial Vehicle (UAV). Further, the experimental results are performed on Faster-RCNN, SSD and YOLO models. The Yolov5 model is found to outperform all other models with 61% precision and 44% of F measure value.

show abstract