1,650 research outputs found
Self-Supervised Predictive Convolutional Attentive Block for Anomaly Detection
Anomaly detection is commonly pursued as a one-class classification problem,
where models can only learn from normal training samples, while being evaluated
on both normal and abnormal test samples. Among the successful approaches for
anomaly detection, a distinguished category of methods relies on predicting
masked information (e.g. patches, future frames, etc.) and leveraging the
reconstruction error with respect to the masked information as an abnormality
score. Different from related methods, we propose to integrate the
reconstruction-based functionality into a novel self-supervised predictive
architectural building block. The proposed self-supervised block is generic and
can easily be incorporated into various state-of-the-art anomaly detection
methods. Our block starts with a convolutional layer with dilated filters,
where the center area of the receptive field is masked. The resulting
activation maps are passed through a channel attention module. Our block is
equipped with a loss that minimizes the reconstruction error with respect to
the masked area in the receptive field. We demonstrate the generality of our
block by integrating it into several state-of-the-art frameworks for anomaly
detection on image and video, providing empirical evidence that shows
considerable performance improvements on MVTec AD, Avenue, and ShanghaiTech. We
release our code as open source at https://github.com/ristea/sspcab.Comment: Accepted at CVPR 2022. Paper + supplementary (14 pages, 9 figures
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
Two-stage sparse representation based abnormal crowd event detection in videos
Ubiquitous surveillance has become part of our lives to increase security and safety. Despite the wide application of surveillance systems, their efficiency is limited by human factors, such as boredom and fatigue; because most of the time, nothing unusual happens. In safety-critical applications, time is essential and it is vital to act fast to prevent costly incidents. This thesis proposes a two-stage abnormal crowd event detection framework based on k-means clustering in the first stage, and sparse representation based methods in the second stage, to alleviate the laborious task of video monitoring.
We conduct a literature review of 18 studies, where we specifically focus on sparse representation based methods. Accordingly, we choose the spatio-temporal gradient feature due to its simplicity, efficiency, and effectiveness in motion representation. After extracting features only from normal events, k-means clustering is applied to separate different motion feature clusters. Then, clusters with smaller samples, which are deemed to contain mostly abnormal features, are removed according to a threshold. In the second stage, we learn a dictionary for each remaining cluster using the approximate K-SVD algorithm. In testing, the reconstruction error of a feature against a learned dictionary and its sparse representation is used to determine an abnormality. We conduct extensive experiments on a standard dataset to evaluate the detection performance of the method. Furthermore, the effect of hyper-parameters in our method is investigated. We also compare our method with different methods to examine its effectiveness.
Results indicate that our abnormal event detection framework can successfully understand abnormal events in a scene while running in real-time at 161 frames per second. With a few exceptions, no significant advantage of the two-stage sparse representation approach over a single large dictionary was found. We speculate that these results may be influenced by a small sample size. Nevertheless, our approach, due to its unsupervised nature, can be adapted to different contexts without additional annotation effort and using only normal events from videos. Therefore it motivates us for further development
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
Exploiting Cross Domain Relationships for Target Recognition
Cross domain recognition extracts knowledge from one domain to recognize samples from another domain of interest. The key to solving problems under this umbrella is to find out the latent connections between different domains. In this dissertation, three different cross domain recognition problems are studied by exploiting the relationships between different domains explicitly according to the specific real problems.
First, the problem of cross view action recognition is studied. The same action might seem quite different when observed from different viewpoints. Thus, how to use the training samples from a given camera view and perform recognition in another new view is the key point. In this work, reconstructable paths between different views are built to mirror labeled actions from one source view into one another target view for learning an adaptable classifier. The path learning takes advantage of the joint dictionary learning techniques with exploiting hidden information in the seemingly useless samples, making the recognition performance robust and effective.
Second, the problem of person re-identification is studied, which tries to match pedestrian images in non-overlapping camera views based on appearance features. In this work, we propose to learn a random kernel forest to discriminatively assign a specific distance metric to each pair of local patches from the two images in matching. The forest is composed by multiple decision trees, which are designed to partition the overall space of local patch-pairs into substantial subspaces, where a simple but effective local metric kernel can be defined to minimize the distance of true matches.
Third, the problem of multi-event detection and recognition in smart grid is studied. The signal of multi-event might not be a straightforward combination of some single-event signals because of the correlation among devices. In this work, a concept of ``root-pattern\u27\u27 is proposed that can be extracted from a collection of single-event signals, but also transferable to analyse the constituent components of multi-cascading-event signals based on an over-complete dictionary, which is designed according to the ``root-patterns\u27\u27 with temporal information subtly embedded.
The correctness and effectiveness of the proposed approaches have been evaluated by extensive experiments
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