25,112 research outputs found
Novelty detection in video surveillance using hierarchical neural networks
Abstract. A hierarchical self-organising neural network is described for the detection of unusual pedestrian behaviour in video-based surveillance systems. The system is trained on a normal data set, with no prior information about the
scene under surveillance, thereby requiring minimal user input. Nodes use a trace activation rule and feedforward connections, modified so that higher layer nodes are sensitive to trajectory segments traced across the previous layer. Top layer nodes have binary lateral connections and corresponding “novelty accumulator” nodes. Lateral connections are set between co-occurring nodes, generating a signal to prevent accumulation of the novelty measure along normal sequences. In abnormal sequences the novelty accumulator nodes are allowed to increase their activity, generating an alarm state
Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks
Detecting and classifying targets in video streams from surveillance cameras
is a cumbersome, error-prone and expensive task. Often, the incurred costs are
prohibitive for real-time monitoring. This leads to data being stored locally
or transmitted to a central storage site for post-incident examination. The
required communication links and archiving of the video data are still
expensive and this setup excludes preemptive actions to respond to imminent
threats. An effective way to overcome these limitations is to build a smart
camera that transmits alerts when relevant video sequences are detected. Deep
neural networks (DNNs) have come to outperform humans in visual classifications
tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be
extended to make use of higher-dimensional input data such as multispectral
data. We explore this opportunity in terms of achievable accuracy and required
computational effort. To analyze the precision of DNNs for scene labeling in an
urban surveillance scenario we have created a dataset with 8 classes obtained
in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR
snapshot sensor to assess the potential of multispectral image data for target
classification. We evaluate several new DNNs, showing that the spectral
information fused together with the RGB frames can be used to improve the
accuracy of the system or to achieve similar accuracy with a 3x smaller
computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even
for scarcely occurring, but particularly interesting classes, such as cars, 75%
of the pixels are labeled correctly with errors occurring only around the
border of the objects. This high accuracy was obtained with a training set of
only 30 labeled images, paving the way for fast adaptation to various
application scenarios.Comment: Presented at SPIE Security + Defence 2016 Proc. SPIE 9997, Target and
Background Signatures I
The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism
Computer vision and other biometrics data science applications have commenced
a new project of profiling people. Rather than using 'transaction generated
information', these systems measure the 'real world' and produce an assessment
of the 'world state' - in this case an assessment of some individual trait.
Instead of using proxies or scores to evaluate people, they increasingly deploy
a logic of revealing the truth about reality and the people within it. While
these profiling knowledge claims are sometimes tentative, they increasingly
suggest that only through computation can these excesses of reality be captured
and understood. This article explores the bases of those claims in the systems
of measurement, representation, and classification deployed in computer vision.
It asks if there is something new in this type of knowledge claim, sketches an
account of a new form of computational empiricism being operationalised, and
questions what kind of human subject is being constructed by these
technological systems and practices. Finally, the article explores legal
mechanisms for contesting the emergence of computational empiricism as the
dominant knowledge platform for understanding the world and the people within
it
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