Robust Real-Time Recognition of Action Sequences Using a Multi-Camera Network

Real-time identification of human activities in urban environments is increasingly becoming important in the context of public safety and national security. Distributed camera networks that provide multiple views of a scene are ideally suited for real-time action recognition. However, deployments of multi-camera based real-time action recognition systems have thus far been inhibited because of several practical issues and restrictive assumptions that are typically made such as the knowledge of a subjects orientation with respect to the cameras, the duration of each action and the conformation of a network deployment during the testing phase to that of a training deployment. In reality, action recognition involves classification of continuously streaming data from multiple views which consists of an interleaved sequence of various human actions. While there has been extensive research on machine learning techniques for action recognition from a single view, the issues arising in the fusion of data from multiple views for reliable action recognition have not received as much attention. In this thesis, I have developed a fusion framework for human action recognition using a multi-camera network that addresses these practical issues of unknown subject orientation, unknown view configurations, action interleaving and variable duration actions.;The proposed framework consists of two components: (1) a score-fusion technique that utilizes underlying view-specific supervised learning classifiers to classify an action within a given set of frames and (2) a sliding window technique that is used to parse a sequence of frames into multiple actions. The use of a score-fusion technique as opposed to a feature-level fusion of data from multiple views allows us to robustly classify actions even when camera configurations are arbitrary and different from training phase and at the same time reduces the required network bandwidth for data transmission permitting wireless deployments. Moreover, the proposed framework is independent of the underlying classifier that is used to generate scores for each action snippet and thus offers more flexibility compared to sequential approaches like Hidden Markov Models. The amount of training and parameterization is also significantly lower compared to HMM-based approaches. This Real-Time recognition system has been tested on 4 classifiers which are Linear Discriminant Analysis, Multinomial Naive Bayes, Logistic Regression and Support Vector Machines. Over 90% accuracy has been achieved by this system in Real-Time recognizing variable duration actions performed by the subject. The performance of the system is also shown to be robust to camera failures

Real-time marker-less multi-person 3D pose estimation in RGB-Depth camera networks

This paper proposes a novel system to estimate and track the 3D poses of multiple persons in calibrated RGB-Depth camera networks. The multi-view 3D pose of each person is computed by a central node which receives the single-view outcomes from each camera of the network. Each single-view outcome is computed by using a CNN for 2D pose estimation and extending the resulting skeletons to 3D by means of the sensor depth. The proposed system is marker-less, multi-person, independent of background and does not make any assumption on people appearance and initial pose. The system provides real-time outcomes, thus being perfectly suited for applications requiring user interaction. Experimental results show the effectiveness of this work with respect to a baseline multi-view approach in different scenarios. To foster research and applications based on this work, we released the source code in OpenPTrack, an open source project for RGB-D people tracking.Comment: Submitted to the 2018 IEEE International Conference on Robotics and Automatio

Viewfinder: final activity report

The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources. The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation. The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein