In video understanding, action spotting consists in temporally localizing human-induced events annotated with single timestamps. In this paper, we propose a novel loss function that specifically considers the temporal context naturally present around each action, rather than focusing on the single annotated frame to spot. We benchmark our loss on a large dataset of soccer videos, SoccerNet, and achieve an improvement of 12.8% over the baseline. We show the generalization capability of our loss for generic activity proposals and detection on ActivityNet, by spotting the beginning and the end of each activity. Furthermore, we provide an extended ablation study and display challenging cases for action spotting in soccer videos. Finally, we qualitatively illustrate how our loss induces a precise temporal understanding of actions and show how such semantic knowledge can be used for automatic highlights generation.

This paper presents a robust occupancy analysis system for thermal imaging. Reliable detection of people is very hard in crowded scenes, due to occlusions and segmentation problems. We therefore propose a framework that optimises the occupancy analysis over long periods by including information on the transition in occupancy, when people enter or leave the monitored area. In stable periods, with no activity close to the borders, people are detected and counted which contributes to a weighted histogram. When activity close to the border is detected, local tracking is applied in order to identify a crossing. After a full sequence, the number of people during all periods are estimated using a probabilistic graph search optimisation. The system is tested on a total of 51,000 frames, captured in sports arenas. The mean error for a 30-minute period containing 3-13 people is 4.44 %, which is a half of the error percentage optained by detection only, and better than the results of comparable work. The framework is also tested on a public available dataset from an outdoor scene, which proves the generality of the method.

This paper presents a full-scale experiment investigating the use of human motion intensities as input for interactive illumination of a town square in the city of Aalborg in Denmark. As illuminators sixteen 3.5 meter high RGB LED lamps were used. The activity on the square was monitored by three thermal cameras and analysed by computer vision software from which motion intensity maps and peoples trajectories were estimated and used as input to control the interactive illumination. The paper introduces a 2-layered interactive light strategy addressing ambient and effect illumination criteria totally four light scenarios were designed and tested. The result shows that in general people immersed in the street lighting did not notice that the light changed according to their presence or actions, but people watching from the edge of the square noticed the interaction between the illumination and the immersed persons. The experiment also demonstrated that interactive can give significant power savings. In the current experiment there was a difference of 92% between the most and less energy consuming light scenario.