2,864 research outputs found
leave a trace - A People Tracking System Meets Anomaly Detection
Video surveillance always had a negative connotation, among others because of
the loss of privacy and because it may not automatically increase public
safety. If it was able to detect atypical (i.e. dangerous) situations in real
time, autonomously and anonymously, this could change. A prerequisite for this
is a reliable automatic detection of possibly dangerous situations from video
data. This is done classically by object extraction and tracking. From the
derived trajectories, we then want to determine dangerous situations by
detecting atypical trajectories. However, due to ethical considerations it is
better to develop such a system on data without people being threatened or even
harmed, plus with having them know that there is such a tracking system
installed. Another important point is that these situations do not occur very
often in real, public CCTV areas and may be captured properly even less. In the
artistic project leave a trace the tracked objects, people in an atrium of a
institutional building, become actor and thus part of the installation.
Visualisation in real-time allows interaction by these actors, which in turn
creates many atypical interaction situations on which we can develop our
situation detection. The data set has evolved over three years and hence, is
huge. In this article we describe the tracking system and several approaches
for the detection of atypical trajectories
Pedestrian localization, tracking and behavior analysis from multiple cameras
Video surveillance is currently undergoing a rapid growth. However, while thousands of cameras are being installed in public places all over the world, computer programs that could reliably detect and track people in order to analyze their behavior are not yet operational. Challenges are numerous, ranging from low image quality, suboptimal scene lighting, changing appearances of pedestrians, occlusions with environment and between people, complex interacting trajectories in crowds, etc. In this thesis, we propose a complete approach for detecting and tracking an unknown number of interacting people from multiple cameras located at eye level. Our system works reliably in spite of significant occlusions and delivers metrically accurate trajectories for each tracked individual. Furthermore, we develop a method for representing the most common types of motion in a specific environment and learning them automatically from image data. We demonstrate that a generative model for detection can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. We then advocate that multi-people tracking can be achieved by detecting people in individual frames and then linking detections across frames. We formulate the linking step as a problem of finding the most probable state of a hidden Markov process given the set of images and frame-independent detections. We first propose to solve this problem by optimizing trajectories independently with Dynamic Programming. In a second step, we reformulate the problem as a constrained flow optimization resulting in a convex problem that can be solved using standard Linear Programming techniques and is far simpler formally and algorithmically than existing techniques. We show that the particular structure of this framework lets us solve it equivalently using the k-shortest paths algorithm, which leads to a much faster optimization. Finally, we introduce a novel behavioral model to describe pedestrians motions, which is able to capture sophisticated motion patterns resulting from the mixture of different categories of random trajectories. Due to its simplicity, this model can be learned from video sequences in a totally unsupervised manner through an Expectation-Maximization procedure. We show that this behavior model can be used to make tracking systems more robust in ambiguous situations. Moreover, we demonstrate its ability to characterize and detect atypical individual motions
Multi-scale Deep Learning Architectures for Person Re-identification
Person Re-identification (re-id) aims to match people across non-overlapping
camera views in a public space. It is a challenging problem because many people
captured in surveillance videos wear similar clothes. Consequently, the
differences in their appearance are often subtle and only detectable at the
right location and scales. Existing re-id models, particularly the recently
proposed deep learning based ones match people at a single scale. In contrast,
in this paper, a novel multi-scale deep learning model is proposed. Our model
is able to learn deep discriminative feature representations at different
scales and automatically determine the most suitable scales for matching. The
importance of different spatial locations for extracting discriminative
features is also learned explicitly. Experiments are carried out to demonstrate
that the proposed model outperforms the state-of-the art on a number of
benchmarksComment: 9 pages, 3 figures, accepted by ICCV 201
Characterization of Infants' General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study
Cerebral palsy, the most common childhood neuromotor disorder, is often diagnosed through visual assessment of general movements (GM) in infancy. This skill requires extensive training and is thus difficult to implement on a large scale. Automated analysis of GM performed using low-cost instrumentation in the home may be used to estimate quantitative metrics predictive of movement disorders. This study explored if infants' GM may be successfully evaluated in a familiar environment by processing the 3D trajectories of points of interest (PoI) obtained from recordings of a single commercial RGB-D sensor. The RGB videos were processed using an open-source markerless motion tracking method which allowed the estimation of the 2D trajectories of the selected PoI and a purposely developed method which allowed the reconstruction of their 3D trajectories making use of the data recorded with the depth sensor. Eight infants' GM were recorded in the home at 3, 4, and 5 months of age. Eight GM metrics proposed in the literature in addition to a novel metric were estimated from the PoI trajectories at each timepoint. A pediatric neurologist and physiatrist provided an overall clinical evaluation from infants' video. Subsequently, a comparison between metrics and clinical evaluation was performed. The results demonstrated that GM metrics may be meaningfully estimated and potentially used for early identification of movement disorders
A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
Extracting behavioral measurements non-invasively from video is stymied by
the fact that it is a hard computational problem. Recent advances in deep
learning have tremendously advanced predicting posture from videos directly,
which quickly impacted neuroscience and biology more broadly. In this primer we
review the budding field of motion capture with deep learning. In particular,
we will discuss the principles of those novel algorithms, highlight their
potential as well as pitfalls for experimentalists, and provide a glimpse into
the future.Comment: Review, 21 pages, 8 figures and 5 boxe
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