15,060 research outputs found
3D People Surveillance on Range Data Sequences of a Rotating Lidar
In this paper, we propose an approach on real-time 3D people surveillance, with probabilistic foreground modeling, multiple person tracking and on-line re-identification. Our principal aim is to demonstrate the capabilities of a special range sensor, called rotating multi-beam (RMB) Lidar, as a future possible surveillance camera. We present methodological contributions in two key issues. First, we introduce a hybrid 2D--3D method for robust foreground-background classification of the recorded RMB-Lidar point clouds, with eliminating spurious effects resulted by quantification error of the discretized view angle, non-linear position corrections of sensor calibration, and background flickering, in particularly due to motion of vegetation. Second, we propose a real-time method for moving pedestrian detection and tracking in RMB-Lidar sequences of dense surveillance scenarios, with short- and long-term object assignment. We introduce a novel person re-identification algorithm based on solely the Lidar measurements, utilizing in parallel the range and the intensity channels of the sensor, which provide biometric features. Quantitative evaluation is performed on seven outdoor Lidar sequences containing various multi-target scenarios displaying challenging outdoor conditions with low point density and multiple occlusions
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
Current multi-person localisation and tracking systems have an over reliance
on the use of appearance models for target re-identification and almost no
approaches employ a complete deep learning solution for both objectives. We
present a novel, complete deep learning framework for multi-person localisation
and tracking. In this context we first introduce a light weight sequential
Generative Adversarial Network architecture for person localisation, which
overcomes issues related to occlusions and noisy detections, typically found in
a multi person environment. In the proposed tracking framework we build upon
recent advances in pedestrian trajectory prediction approaches and propose a
novel data association scheme based on predicted trajectories. This removes the
need for computationally expensive person re-identification systems based on
appearance features and generates human like trajectories with minimal
fragmentation. The proposed method is evaluated on multiple public benchmarks
including both static and dynamic cameras and is capable of generating
outstanding performance, especially among other recently proposed deep neural
network based approaches.Comment: To appear in IEEE Winter Conference on Applications of Computer
Vision (WACV), 201
A Neural System for Automated CCTV Surveillance
This paper overviews a new system, the “Owens
Tracker,” for automated identification of suspicious
pedestrian activity in a car-park.
Centralized CCTV systems relay multiple video streams
to a central point for monitoring by an operator. The
operator receives a continuous stream of information,
mostly related to normal activity, making it difficult to
maintain concentration at a sufficiently high level.
While it is difficult to place quantitative boundaries on
the number of scenes and time period over which
effective monitoring can be performed, Wallace and
Diffley [1] give some guidance, based on empirical and
anecdotal evidence, suggesting that the number of
cameras monitored by an operator be no greater than 16,
and that the period of effective monitoring may be as
low as 30 minutes before recuperation is required.
An intelligent video surveillance system should
therefore act as a filter, censuring inactive scenes and
scenes showing normal activity. By presenting the
operator only with unusual activity his/her attention is
effectively focussed, and the ratio of cameras to
operators can be increased.
The Owens Tracker learns to recognize environmentspecific
normal behaviour, and refers sequences of
unusual behaviour for operator attention. The system
was developed using standard low-resolution CCTV
cameras operating in the car-parks of Doxford Park
Industrial Estate (Sunderland, Tyne and Wear), and
targets unusual pedestrian behaviour.
The modus operandi of the system is to highlight
excursions from a learned model of normal behaviour in
the monitored scene. The system tracks objects and
extracts their centroids; behaviour is defined as the
trajectory traced by an object centroid; normality as the
trajectories typically encountered in the scene. The
essential stages in the system are: segmentation of
objects of interest; disambiguation and tracking of
multiple contacts, including the handling of occlusion
and noise, and successful tracking of objects that
“merge” during motion; identification of unusual
trajectories. These three stages are discussed in more
detail in the following sections, and the system
performance is then evaluated
Pedestrian Attribute Recognition: A Survey
Recognizing pedestrian attributes is an important task in computer vision
community due to it plays an important role in video surveillance. Many
algorithms has been proposed to handle this task. The goal of this paper is to
review existing works using traditional methods or based on deep learning
networks. Firstly, we introduce the background of pedestrian attributes
recognition (PAR, for short), including the fundamental concepts of pedestrian
attributes and corresponding challenges. Secondly, we introduce existing
benchmarks, including popular datasets and evaluation criterion. Thirdly, we
analyse the concept of multi-task learning and multi-label learning, and also
explain the relations between these two learning algorithms and pedestrian
attribute recognition. We also review some popular network architectures which
have widely applied in the deep learning community. Fourthly, we analyse
popular solutions for this task, such as attributes group, part-based,
\emph{etc}. Fifthly, we shown some applications which takes pedestrian
attributes into consideration and achieve better performance. Finally, we
summarized this paper and give several possible research directions for
pedestrian attributes recognition. The project page of this paper can be found
from the following website:
\url{https://sites.google.com/view/ahu-pedestrianattributes/}.Comment: Check our project page for High Resolution version of this survey:
https://sites.google.com/view/ahu-pedestrianattributes
Multiple object tracking using a neural cost function
This paper presents a new approach to the tracking of multiple objects in CCTV surveillance using a combination of simple neural cost functions based on Self-Organizing Maps, and a greedy assignment algorithm. Using a reference standard data set and an exhaustive search algorithm for benchmarking, we show that the cost function plays the most significant role in realizing high levels of performance. The neural cost function’s context-sensitive treatment of appearance, change of appearance and trajectory yield better tracking than a simple, explicitly designed cost function. The algorithm matches 98.8% of objects to within 15 pixels
Online Metric-Weighted Linear Representations for Robust Visual Tracking
In this paper, we propose a visual tracker based on a metric-weighted linear
representation of appearance. In order to capture the interdependence of
different feature dimensions, we develop two online distance metric learning
methods using proximity comparison information and structured output learning.
The learned metric is then incorporated into a linear representation of
appearance.
We show that online distance metric learning significantly improves the
robustness of the tracker, especially on those sequences exhibiting drastic
appearance changes. In order to bound growth in the number of training samples,
we design a time-weighted reservoir sampling method.
Moreover, we enable our tracker to automatically perform object
identification during the process of object tracking, by introducing a
collection of static template samples belonging to several object classes of
interest. Object identification results for an entire video sequence are
achieved by systematically combining the tracking information and visual
recognition at each frame. Experimental results on challenging video sequences
demonstrate the effectiveness of the method for both inter-frame tracking and
object identification.Comment: 51 pages. Appearing in IEEE Transactions on Pattern Analysis and
Machine Intelligenc
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