Abstract-In this paper, we propose to show how video data available in standard CCTV transportation systems can represent a useful source of information for transportation infrastructure management, optimization and planning if adequately analyzed (e.g. to facilitate equipment usage understanding, to ease diagnostic and planning for system managers). More precisely, we present two algorithms allowing to estimate the number of people in a camera view and to measure the platform time-occupancy by trains. A statistical analysis of the results of each algorithm provide interesting insights regarding station usage. It is also shown that combining information from the algorithms in different views provide a finer understanding of the station usage. An end-user point of view confirms the interest of the proposed analysis.
Human hair is a crucial biometric characteristic with rich color and texture information. In this paper, we propose a novel hair segmentation approach integrating a deep shape prior into a carefully designed two-stage Fully Convolutional Neural Network (FCNN) pipeline. First, we utilize a FCNN with an Atrous Spatial Pyramid Pooling (ASPP) module to train a human hair shape prior based on a specific distance transform. In the second stage, we combine the hair shape prior and the original image to form the input of a symmetric encoder-decoder FCNN with a border refinement module to get the final hair segmentation output. Both quantitative and qualitative results show that our method achieves state-of-the-art performance on the LFW-Part and Figaro1k datasets.
One of the promises of Digital Television is the possibility of creating interactive and innovative television services, like catch-up TV. However, these services need external resources, coming from the channels themselves or from manual annotation. In this paper, a system for automatically building a Catch-up TV service from the available EPG and the broadcasted TV stream, is presented. The system combines several content-based techniques for extracting exact program boundaries from the TV stream. Traditional commercial detection and recognition methods are used, as well as novel techniques to detect and classify repetitions. Identification of the TV program is then performed by matching the detected boundaries with the EPG. Extensive experiments on three weeks of TV assess the effectiveness of the proposed system.
This paper presents a method for monitoring activities at a ticket vending machine in a video-surveillance context. Rather than relying on the output of a tracking module, which is prone to errors, the events are direclty recognized from image measurements. This especially does not require tracking. A statistical layered approach is proposed, where in the first layer, several sub-events are defined and detected using a discriminative approach. The second layer uses the result of the first and models the temporal relationships of the highlevel event using a Hidden Markov Model (HMM). Results are assessed on 3h30 hours of real video footage coming from Turin metro station.
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