Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking

Current multi-person localisation and tracking systems have an over reliance on the use of appearance models for target re-identification and almost no approaches employ a complete deep learning solution for both objectives. We present a novel, complete deep learning framework for multi-person localisation and tracking. In this context we first introduce a light weight sequential Generative Adversarial Network architecture for person localisation, which overcomes issues related to occlusions and noisy detections, typically found in a multi person environment. In the proposed tracking framework we build upon recent advances in pedestrian trajectory prediction approaches and propose a novel data association scheme based on predicted trajectories. This removes the need for computationally expensive person re-identification systems based on appearance features and generates human like trajectories with minimal fragmentation. The proposed method is evaluated on multiple public benchmarks including both static and dynamic cameras and is capable of generating outstanding performance, especially among other recently proposed deep neural network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer Vision (WACV), 201

Out there and in here: design for blended scientific inquiry learning

One of the benefits of mobile technologies is to combine ‘the digital’ (e.g., data, information, photos) with ‘field’ experiences in novel ways that are contextualized by people’s current located activities. However, often cost, mobility disabilities and time exclude students from engaging in such peripatetic experiences. The Out There and In Here project, is exploring a combination of mobile and tabletop technologies in support for collaborative learning. A system is being developed for synchronous collaboration between geology students in the field and peers at an indoor location. The overarching goal of this research is to develop technologies that support people working together in a suitable manner for their locations. There are two OTIH project research threads. The first deals with disabled learner access issues: these complex issues are being reviewed in subsequent evaluations and publications. This paper will deal with issues of technology supported learning design for remote and co-located science learners. Several stakeholder evaluations and two field trials have reviewed two research questions: 1. What will enhance the learning experience for those in the field and laboratory? 2. How can learning trajectories and appropriate technologies be designed to support equitable co-located and remote learning collaboration? This paper focuses on describing the iterative linked development of technologies and scientific inquiry pedagogy. Two stages within the research project are presented. The 1st stage details several pilot studies over 3 years with 21 student participants in synchronous collaborations with traditional technology and pedagogical models. Findings revealed that this was an engaging and useful experience although issues of equity in collaboration needed further research. The 2nd stage, in this project, has been to evaluate data from over 25 stakeholders (academics, learning and technology designers) to develop pervasive ambient technological solutions supporting orchestration of mixed levels of pedagogy (i.e. abstract synthesis to specific investigation). Middleware between tabletop ‘surface’ technologies and mobile devices are being designed with Microsoft and OOKL (a mobile software company) to support these developments. Initial findings reveal issues around equity, ownership and professional identity

A generic framework for video understanding applied to group behavior recognition

This paper presents an approach to detect and track groups of people in video-surveillance applications, and to automatically recognize their behavior. This method keeps track of individuals moving together by maintaining a spacial and temporal group coherence. First, people are individually detected and tracked. Second, their trajectories are analyzed over a temporal window and clustered using the Mean-Shift algorithm. A coherence value describes how well a set of people can be described as a group. Furthermore, we propose a formal event description language. The group events recognition approach is successfully validated on 4 camera views from 3 datasets: an airport, a subway, a shopping center corridor and an entrance hall.Comment: (20/03/2012