Modelling of interactions for the recognition of activities in groups of people

In this research study we adopt a probabilistic modelling of interactions in groups of people, using video sequences, leading to the recognition of their activities. Firstly, we model short smooth streams of localised movement. Afterwards, we partition the scene in regions of distinct movement, by using maximum a posteriori estimation, by fitting Gaussian Mixture Models (GMM) to the movement statistics. Interactions between moving regions are modelled using the Kullback–Leibler (KL) divergence between pairs of statistical representations of moving regions. Such interactions are considered with respect to the relative movement, moving region location and relative size, as well as to the dynamics of the movement and location inter-dependencies, respectively. The proposed methodology is assessed on two different data sets showing different categories of human interactions and group activities

Recognizing human group activities with localized causalities

10.1109/CVPRW.2009.52068532009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 20091470-147

3D Robotic Sensing of People: Human Perception, Representation and Activity Recognition

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives. As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical human-centered robotics applications. This research focuses on robotic sensing of people, that is, how robots can perceive and represent humans and understand their behaviors, primarily through 3D robotic vision. In this dissertation, I begin with a broad perspective on human-centered robotics by discussing its real-world applications and significant challenges. Then, I will introduce a real-time perception system, based on the concept of Depth of Interest, to detect and track multiple individuals using a color-depth camera that is installed on moving robotic platforms. In addition, I will discuss human representation approaches, based on local spatio-temporal features, including new “CoDe4D” features that incorporate both color and depth information, a new “SOD” descriptor to efficiently quantize 3D visual features, and the novel AdHuC features, which are capable of representing the activities of multiple individuals. Several new algorithms to recognize human activities are also discussed, including the RG-PLSA model, which allows us to discover activity patterns without supervision, the MC-HCRF model, which can explicitly investigate certainty in latent temporal patterns, and the FuzzySR model, which is used to segment continuous data into events and probabilistically recognize human activities. Cognition models based on recognition results are also implemented for decision making that allow robotic systems to react to human activities. Finally, I will conclude with a discussion of future directions that will accelerate the upcoming technological revolution of human-centered robotics

A Big Bang Big Crunch Type-2 Fuzzy Logic System for Machine Vision-Based Event Detection and Summarization in Real-world Ambient Assisted Living

The recent years have witnessed the prevalence and abundance of vision sensors in various applications such as security surveillance, healthcare and Ambient Assisted Living (AAL) among others. This is so as to realize intelligent environments which are capable of detecting users’ actions and gestures so that the needed services can be provided automatically and instantly to maximize user comfort and safety as well as to minimize energy. However, it is very challenging to automatically detect important events and human behaviour from vision sensors and summarize them in real time. This is due to the massive data sizes related to video analysis applications and the high level of uncertainties associated with the real world unstructured environments occupied by various users. Machine vision based systems can help detect and summarize important information which cannot be detected by any other sensor; for example, how much water a candidate drank and whether or not they had something to eat. However, conventional non-fuzzy based methods are not robust enough to recognize the various complex types of behaviour in AAL applications. Fuzzy logic system (FLS) is an established field of research to robustly handle uncertainties in complicated real-world problems. In this thesis, we will present a general recognition and classification framework based on fuzzy logic systems which allows for behaviour recognition and event summarisation using 2D/3D video sensors in AAL applications. I started by investigating the use of 2D CCTV camera based system where I proposed and developed novel IT2FLS-based methods for silhouette extraction and 2D behaviour recognition which outperform the traditional on the publicly available Weizmann human action dataset. I will also present a novel system based on 3D RGB-D vision sensors and Interval Type-2 Fuzzy Logic based Systems (IT2FLSs) ) generated by the Big Bang Big Crunch (BB-BC) algorithm for the real time automatic detection and summarization of important events and human behaviour. I will present several real world experiments which were conducted for AAL related behaviour with various users. It will be shown that the proposed BB-BC IT2FLSs outperforms its Type-1 FLSs (T1FLSs) counterpart as well as other conventional non-fuzzy methods, and that performance improvement rises when the number of subjects increases. It will be shown that by utilizing the recognized output activity together with relevant event descriptions (such as video data, timestamp, location and user identification) detailed events are efficiently summarized and stored in our back-end SQL event database, which provides services including event searching, activity retrieval and high-definition video playback to the front-end user interfaces

Interactive Tracking, Prediction, and Behavior Learning of Pedestrians in Dense Crowds

The ability to automatically recognize human motions and behaviors is a key skill for autonomous machines to exhibit to interact intelligently with a human-inhabited environment. The capabilities autonomous machines should have include computing the motion trajectory of each pedestrian in a crowd, predicting his or her position in the near future, and analyzing the personality characteristics of the pedestrian. Such techniques are frequently used for collision-free robot navigation, data-driven crowd simulation, and crowd surveillance applications. However, prior methods for these problems have been restricted to low-density or sparse crowds where the pedestrian movement is modeled using simple motion models. In this thesis, we present several interactive algorithms to extract pedestrian trajectories from videos in dense crowds. Our approach combines different pedestrian motion models with particle tracking and mixture models and can obtain an average of $20\%$ improvement in accuracy in medium-density crowds over prior work. We compute the pedestrian dynamics from these trajectories using Bayesian learning techniques and combine them with global methods for long-term pedestrian prediction in densely crowded settings. Finally, we combine these techniques with Personality Trait Theory to automatically classify the dynamic behavior or the personality of a pedestrian based on his or her movements in a crowded scene. The resulting algorithms are robust and can handle sparse and noisy motion trajectories. We demonstrate the benefits of our long-term prediction and behavior classification methods in dense crowds and highlight the benefits over prior techniques. We highlight the performance of our novel algorithms on three different applications. The first application is interactive data-driven crowd simulation, which includes crowd replication as well as the combination of pedestrian behaviors from different videos. Secondly, we combine the prediction scheme with proxemic characteristics from psychology and use them to perform socially-aware navigation. Finally, we present novel techniques for anomaly detection in low-to medium-density crowd videos using trajectory-level behavior learning.Doctor of Philosoph

Understanding Complex Human Behaviour in Images and Videos.

Understanding human motions and activities in images and videos is an important problem in many application domains, including surveillance, robotics, video indexing, and sports analysis. Although much progress has been made in classifying single person's activities in simple videos, little efforts have been made toward the interpretation of behaviors of multiple people in natural videos. In this thesis, I will present my research endeavor toward the understanding of behaviors of multiple people in natural images and videos. I identify four major challenges in this problem: i) identifying individual properties of people in videos, ii) modeling and recognizing the behavior of multiple people, iii) understanding human activities in multiple levels of resolutions and iv) learning characteristic patterns of interactions between people or people and surrounding environment. I discuss how we solve these challenging problems using various computer vision and machine learning technologies. I conclude with final remarks, observations, and possible future research directions.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99956/1/wgchoi_1.pd