PETS2010: Dataset and Challenge

Abstract

This paper describes the dataset and vision challenges that form part of the PETS 2014 workshop. The datasets are multisensor sequences containing different activities around a parked vehicle in a parking lot. The dataset scenarios were filmed from multiple cameras mounted on the vehicle itself and involve multiple actors. In PETS2014 workshop, 22 acted scenarios are provided of abnormal behaviour around the parked vehicle. The aim in PETS 2014 is to provide a standard benchmark that indicates how detection, tracking, abnormality and behaviour analysis systems perform against a common database. The dataset specifically addresses several vision challenges corresponding to different steps in a video understanding system: Low-Level Video Analysis (object detection and tracking), Mid-Level Video Analysis ('simple' event detection: the behaviour recognition of a single actor) and High-Level Video Analysis ('complex' event detection: the behaviour and interaction recognition of several actors).

Real-time person counting by propagating networks flows

“…Furthermore, we evaluated the performance of the system on crowded environments by applying it to one of the challenging sequences of the PETS-benchmark [6] on people counting and compared it to the system published by [8].…”

Multilevel Chinese Takeaway Process and Label-Based Processes for Rule Induction in the Context of Automated Sports Video Annotation

“…Additionally, α also defines the self-transition probability for each state. The second hyper-parameter γ, is employed in the Beta distribution of (13) and controls the size of the stick-break defined in (11), which furthermore defines the contribution of the remaining probability.…”

“…Application domains vary from surveillance [11] to processing entertainment movies [29] and TV shows [34]. Sign language recognition has also been explored [6].…”

Multi-source Multi-scale Counting in Extremely Dense Crowd Images

“…Some methods proposed in literature for crowd detection perform image segmentation without actual counting or localization [1], while others simply estimate the coarse density range within local regions [24]. In terms of experimental data, most of the existing algorithms for exact counting have been tested on low to medium density crowds, e.g., USCD dataset with density of 11 − 46 people per frame [4], Mall dataset with density of 13 − 53 individuals per frame [5], and PETS dataset containing 3 − 40 people per frame [9]. In contrast to these images and videos, our algorithm has been tested on still images containing between 94 and 4543 people per image, with an average of 1280 people over fifty images in the dataset.…”