Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

Child Exploitation and the FIFA World Cup: A review of risks and protective interventions

This review was commissioned by the Child Abuse Programme (CAP) of Oak Foundation, a large international philanthropic organisation. It forms part of CAP’s effort to win societal rejection of practices such as the sexual exploitation of children and adolescents around major sporting events (MSEs), and to embed prevention and protection from exploitation as a permanent concern for global sports-related bodies. This review is intended to inform action in countries that host MSEs and to provide some suggestions on how hosting countries can avoid past pitfalls and mistakes in relation to child exploitation, especially economic and sexual exploitation. Importantly, it also acts as a call to action by those responsible for commissioning and staging MSEs, such as FIFA and the IOC, to anticipate, prepare for and adopt risk mitigation strategies and interventions. Positive leadership from these culturally powerful bodies could prove decisive in shifting hearts, minds and actions in the direction of improved safety for children

Over speed detection using Artificial Intelligence

Over speeding is one of the most common traffic violations. Around 41 million people are issued speeding tickets each year in USA i.e one every second. Existing approaches to detect over- speeding are not scalable and require manual efforts. In this project, by the use of computer vision and artificial intelligence, I have tried to detect over speeding and report the violation to the law enforcement officer. It was observed that when predictions are done using YoloV3, we get the best results

Spartan Daily September 1, 2010

Volume 135, Issue 3https://scholarworks.sjsu.edu/spartandaily/1166/thumbnail.jp

State of Play 2016: Trends and Developments

Our first annual report on how well stakeholders are serving children and communities through youth sports offers grades, the latest data on participation rates, exclusive insights, and 50+ key developments in the past year in each of the areas of opportunity. The report also identifies next steps in building the movement to make sport accessible and affordable to all.

Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech