3 research outputs found
Abnormal Event Detection in Videos using Spatiotemporal Autoencoder
We present an efficient method for detecting anomalies in videos. Recent
applications of convolutional neural networks have shown promises of
convolutional layers for object detection and recognition, especially in
images. However, convolutional neural networks are supervised and require
labels as learning signals. We propose a spatiotemporal architecture for
anomaly detection in videos including crowded scenes. Our architecture includes
two main components, one for spatial feature representation, and one for
learning the temporal evolution of the spatial features. Experimental results
on Avenue, Subway and UCSD benchmarks confirm that the detection accuracy of
our method is comparable to state-of-the-art methods at a considerable speed of
up to 140 fps
Misbehaviour Prediction for Autonomous Driving Systems
Deep Neural Networks (DNNs) are the core component of modern autonomous
driving systems. To date, it is still unrealistic that a DNN will generalize
correctly in all driving conditions. Current testing techniques consist of
offline solutions that identify adversarial or corner cases for improving the
training phase, and little has been done for enabling online healing of
DNN-based vehicles. In this paper, we address the problem of estimating the
confidence of DNNs in response to unexpected execution contexts with the
purpose of predicting potential safety-critical misbehaviours such as out of
bound episodes or collisions. Our approach SelfOracle is based on a novel
concept of self-assessment oracle, which monitors the DNN confidence at
runtime, to predict unsupported driving scenarios in advance. SelfOracle uses
autoencoder and time-series-based anomaly detection to reconstruct the driving
scenarios seen by the car, and determine the confidence boundary of
normal/unsupported conditions. In our empirical assessment, we evaluated the
effectiveness of different variants of SelfOracle at predicting injected
anomalous driving contexts, using DNN models and simulation environment from
Udacity. Results show that, overall, SelfOracle can predict 77% misbehaviours,
up to 6 seconds in advance, outperforming the online input validation approach
of DeepRoad by a factor almost equal to 3.Comment: 11 page