5,761 research outputs found
Adapting pedestrian detectors to new domains: A comprehensive review.
Successful detection and localisation of pedestrians is an important goal in computer vision which is a core area in Artificial Intelligence. State-of-the-art pedestrian detectors proposed in literature have reached impressive performance on certain datasets. However, it has been pointed out that these detectors tend not to perform very well when applied to specific scenes that differ from the training datasets in some ways. Due to this, domain adaptation approaches have recently become popular in order to adapt existing detectors to new domains to improve the performance in those domains. There is a real need to review and analyse critically the state-of-the-art domain adaptation algorithms, especially in the area of object and pedestrian detection. In this paper, we survey the most relevant and important state-of-the-art results for domain adaptation for image and video data, with a particular focus on pedestrian detection. Related areas to domain adaptation are also included in our review and we make observations and draw conclusions from the representative papers and give practical recommendations on which methods should be preferred in different situations that practitioners may encounter in real-life
Spatiotemporal Event Graphs for Dynamic Scene Understanding
Dynamic scene understanding is the ability of a computer system to interpret
and make sense of the visual information present in a video of a real-world
scene. In this thesis, we present a series of frameworks for dynamic scene
understanding starting from road event detection from an autonomous driving
perspective to complex video activity detection, followed by continual learning
approaches for the life-long learning of the models. Firstly, we introduce the
ROad event Awareness Dataset (ROAD) for Autonomous Driving, to our knowledge
the first of its kind. Due to the lack of datasets equipped with formally
specified logical requirements, we also introduce the ROad event Awareness
Dataset with logical Requirements (ROAD-R), the first publicly available
dataset for autonomous driving with requirements expressed as logical
constraints, as a tool for driving neurosymbolic research in the area. Next, we
extend event detection to holistic scene understanding by proposing two complex
activity detection methods. In the first method, we present a deformable,
spatiotemporal scene graph approach, consisting of three main building blocks:
action tube detection, a 3D deformable RoI pooling layer designed for learning
the flexible, deformable geometry of the constituent action tubes, and a scene
graph constructed by considering all parts as nodes and connecting them based
on different semantics. In a second approach evolving from the first, we
propose a hybrid graph neural network that combines attention applied to a
graph encoding of the local (short-term) dynamic scene with a temporal graph
modelling the overall long-duration activity. Finally, the last part of the
thesis is about presenting a new continual semi-supervised learning (CSSL)
paradigm.Comment: PhD thesis, Oxford Brookes University, Examiners: Prof. Dima Damen
and Dr. Matthias Rolf, 183 page
Video trajectory analysis using unsupervised clustering and multi-criteria ranking
Surveillance camera usage has increased significantly for visual surveillance. Manual analysis of large video data recorded by cameras may not be feasible on a larger scale. In various applications, deep learning-guided supervised systems are used to track and identify unusual patterns. However, such systems depend on learning which may not be possible. Unsupervised methods relay on suitable features and demand cluster analysis by experts. In this paper, we propose an unsupervised trajectory clustering method referred to as t-Cluster. Our proposed method prepares indexes of object trajectories by fusing high-level interpretable features such as origin, destination, path, and deviation. Next, the clusters are fused using multi-criteria decision making and trajectories are ranked accordingly. The method is able to place abnormal patterns on the top of the list. We have evaluated our algorithm and compared it against competent baseline trajectory clustering methods applied to videos taken from publicly available benchmark datasets. We have obtained higher clustering accuracies on public datasets with significantly lesser computation overhead
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models
Video Anomaly Detection (VAD) serves as a pivotal technology in the
intelligent surveillance systems, enabling the temporal or spatial
identification of anomalous events within videos. While existing reviews
predominantly concentrate on conventional unsupervised methods, they often
overlook the emergence of weakly-supervised and fully-unsupervised approaches.
To address this gap, this survey extends the conventional scope of VAD beyond
unsupervised methods, encompassing a broader spectrum termed Generalized Video
Anomaly Event Detection (GVAED). By skillfully incorporating recent
advancements rooted in diverse assumptions and learning frameworks, this survey
introduces an intuitive taxonomy that seamlessly navigates through
unsupervised, weakly-supervised, supervised and fully-unsupervised VAD
methodologies, elucidating the distinctions and interconnections within these
research trajectories. In addition, this survey facilitates prospective
researchers by assembling a compilation of research resources, including public
datasets, available codebases, programming tools, and pertinent literature.
Furthermore, this survey quantitatively assesses model performance, delves into
research challenges and directions, and outlines potential avenues for future
exploration.Comment: Accepted by ACM Computing Surveys. For more information, please see
our project page: https://github.com/fudanyliu/GVAE
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
Are object detection assessment criteria ready for maritime computer vision?
Maritime vessels equipped with visible and infrared cameras can complement
other conventional sensors for object detection. However, application of
computer vision techniques in maritime domain received attention only recently.
The maritime environment offers its own unique requirements and challenges.
Assessment of the quality of detections is a fundamental need in computer
vision. However, the conventional assessment metrics suitable for usual object
detection are deficient in the maritime setting. Thus, a large body of related
work in computer vision appears inapplicable to the maritime setting at the
first sight. We discuss the problem of defining assessment metrics suitable for
maritime computer vision. We consider new bottom edge proximity metrics as
assessment metrics for maritime computer vision. These metrics indicate that
existing computer vision approaches are indeed promising for maritime computer
vision and can play a foundational role in the emerging field of maritime
computer vision
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