9,253 research outputs found
Activity understanding and unusual event detection in surveillance videos
PhDComputer scientists have made ceaseless efforts to replicate cognitive video understanding abilities
of human brains onto autonomous vision systems. As video surveillance cameras become
ubiquitous, there is a surge in studies on automated activity understanding and unusual event detection
in surveillance videos. Nevertheless, video content analysis in public scenes remained a
formidable challenge due to intrinsic difficulties such as severe inter-object occlusion in crowded
scene and poor quality of recorded surveillance footage. Moreover, it is nontrivial to achieve
robust detection of unusual events, which are rare, ambiguous, and easily confused with noise.
This thesis proposes solutions for resolving ambiguous visual observations and overcoming unreliability
of conventional activity analysis methods by exploiting multi-camera visual context
and human feedback.
The thesis first demonstrates the importance of learning visual context for establishing reliable
reasoning on observed activity in a camera network. In the proposed approach, a new Cross
Canonical Correlation Analysis (xCCA) is formulated to discover and quantify time delayed pairwise
correlations of regional activities observed within and across multiple camera views. This
thesis shows that learning time delayed pairwise activity correlations offers valuable contextual
information for (1) spatial and temporal topology inference of a camera network, (2) robust person
re-identification, and (3) accurate activity-based video temporal segmentation. Crucially, in
contrast to conventional methods, the proposed approach does not rely on either intra-camera or
inter-camera object tracking; it can thus be applied to low-quality surveillance videos featuring
severe inter-object occlusions.
Second, to detect global unusual event across multiple disjoint cameras, this thesis extends
visual context learning from pairwise relationship to global time delayed dependency between
regional activities. Specifically, a Time Delayed Probabilistic Graphical Model (TD-PGM) is
proposed to model the multi-camera activities and their dependencies. Subtle global unusual
events are detected and localised using the model as context-incoherent patterns across multiple
camera views. In the model, different nodes represent activities in different decomposed re3
gions from different camera views, and the directed links between nodes encoding time delayed
dependencies between activities observed within and across camera views. In order to learn optimised
time delayed dependencies in a TD-PGM, a novel two-stage structure learning approach
is formulated by combining both constraint-based and scored-searching based structure learning
methods.
Third, to cope with visual context changes over time, this two-stage structure learning approach
is extended to permit tractable incremental update of both TD-PGM parameters and its
structure. As opposed to most existing studies that assume static model once learned, the proposed
incremental learning allows a model to adapt itself to reflect the changes in the current
visual context, such as subtle behaviour drift over time or removal/addition of cameras. Importantly,
the incremental structure learning is achieved without either exhaustive search in a large
graph structure space or storing all past observations in memory, making the proposed solution
memory and time efficient.
Forth, an active learning approach is presented to incorporate human feedback for on-line
unusual event detection. Contrary to most existing unsupervised methods that perform passive
mining for unusual events, the proposed approach automatically requests supervision for critical
points to resolve ambiguities of interest, leading to more robust detection of subtle unusual
events. The active learning strategy is formulated as a stream-based solution, i.e. it makes decision
on-the-fly on whether to request label for each unlabelled sample observed in sequence.
It selects adaptively two active learning criteria, namely likelihood criterion and uncertainty criterion
to achieve (1) discovery of unknown event classes and (2) refinement of classification
boundary.
The effectiveness of the proposed approaches is validated using videos captured from busy
public scenes such as underground stations and traffic intersections
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Video analytics system for surveillance videos
Developing an intelligent inspection system that can enhance the public safety is challenging. An efficient video analytics system can help monitor unusual events and mitigate possible damage or loss. This thesis aims to analyze surveillance video data, report abnormal activities and retrieve corresponding video clips. The surveillance video dataset used in this thesis is derived from ALERT Dataset, a collection of surveillance videos at airport security checkpoints.
The video analytics system in this thesis can be thought as a pipelined process. The system takes the surveillance video as input, and passes it through a series of processing such as object detection, multi-object tracking, person-bin association and re-identification. In the end, we can obtain trajectories of passengers and baggage in the surveillance videos. Abnormal events like taking away other's belongings will be detected and trigger the alarm automatically. The system could also retrieve the corresponding video clips based on user-defined query
Human behavioural analysis with self-organizing map for ambient assisted living
This paper presents a system for automatically classifying the resting location of a moving object in an indoor environment. The system uses an unsupervised neural network (Self Organising Feature Map) fully implemented on a low-cost, low-power automated home-based surveillance system, capable of monitoring activity level of elders living alone independently. The proposed system runs on an embedded platform with a specialised ceiling-mounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels and to detect specific events such as potential falls. First order motion information, including first order moving average smoothing, is generated from the 2D image coordinates (trajectories). A novel edge-based object detection algorithm capable of running at a reasonable speed on the embedded platform has been developed. The classification is dynamic and achieved in real-time. The dynamic classifier is achieved using a SOFM and a probabilistic model. Experimental results show less than 20% classification error, showing the robustness of our approach over others in literature with minimal power consumption. The head location of the subject is also estimated by a novel approach capable of running on any resource limited platform with power constraints
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