2 research outputs found
CADP: A Novel Dataset for CCTV Traffic Camera based Accident Analysis
This paper presents a novel dataset for traffic accidents analysis. Our goal
is to resolve the lack of public data for research about automatic
spatio-temporal annotations for traffic safety in the roads. Through the
analysis of the proposed dataset, we observed a significant degradation of
object detection in pedestrian category in our dataset, due to the object sizes
and complexity of the scenes. To this end, we propose to integrate contextual
information into conventional Faster R-CNN using Context Mining (CM) and
Augmented Context Mining (ACM) to complement the accuracy for small pedestrian
detection. Our experiments indicate a considerable improvement in object
detection accuracy: +8.51% for CM and +6.20% for ACM. Finally, we demonstrate
the performance of accident forecasting in our dataset using Faster R-CNN and
an Accident LSTM architecture. We achieved an average of 1.684 seconds in terms
of Time-To-Accident measure with an Average Precision of 47.25%. Our Webpage
for the paper is https://goo.gl/cqK2wEComment: Accepted at IEEE International Workshop on Traffic and Street
Surveillance for Safety and Security, First three authors contributed
equally, 7 pages + 1 Reference
"Who is Driving around Me?" Unique Vehicle Instance Classification using Deep Neural Features
Being aware of other traffic is a prerequisite for self-driving cars to
operate in the real world. In this paper, we show how the intrinsic feature
maps of an object detection CNN can be used to uniquely identify vehicles from
a dash-cam feed. Feature maps of a pretrained `YOLO' network are used to create
700 deep integrated feature signatures (DIFS) from 20 different images of 35
vehicles from a high resolution dataset and 340 signatures from 20 different
images of 17 vehicles of a lower resolution tracking benchmark dataset. The
YOLO network was trained to classify general object categories, e.g. classify a
detected object as a `car' or `truck'. 5-Fold nearest neighbor (1NN)
classification was used on DIFS created from feature maps in the middle layers
of the network to correctly identify unique vehicles at a rate of 96.7\% for
the high resolution data and with a rate of 86.8\% for the lower resolution
data. We conclude that a deep neural detection network trained to distinguish
between different classes can be successfully used to identify different
instances belonging to the same class, through the creation of deep integrated
feature signatures (DIFS).Comment: 6 pages, 3 figure