33,021 research outputs found
Under vehicle perception for high level safety measures using a catadioptric camera system
In recent years, under vehicle surveillance and the classification of the vehicles become an indispensable task that must be achieved for security measures in certain areas such as shopping centers, government buildings, army camps etc. The main challenge to achieve this task is to monitor the under
frames of the means of transportations. In this paper, we present a novel solution to achieve this aim. Our solution consists of three main parts: monitoring, detection and classification. In the first part we design a new catadioptric camera system in which the perspective camera points downwards to the catadioptric mirror mounted to the body of a mobile robot. Thanks to the
catadioptric mirror the scenes against the camera optical axis direction can be viewed. In the second part we use speeded up robust features (SURF) in an object recognition algorithm. Fast appearance based mapping algorithm (FAB-MAP) is exploited for the classification of the means of transportations in the third
part. Proposed technique is implemented in a laboratory environment
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Vision-based vehicle detection approaches achieve incredible success in
recent years with the development of deep convolutional neural network (CNN).
However, existing CNN based algorithms suffer from the problem that the
convolutional features are scale-sensitive in object detection task but it is
common that traffic images and videos contain vehicles with a large variance of
scales. In this paper, we delve into the source of scale sensitivity, and
reveal two key issues: 1) existing RoI pooling destroys the structure of small
scale objects, 2) the large intra-class distance for a large variance of scales
exceeds the representation capability of a single network. Based on these
findings, we present a scale-insensitive convolutional neural network (SINet)
for fast detecting vehicles with a large variance of scales. First, we present
a context-aware RoI pooling to maintain the contextual information and original
structure of small scale objects. Second, we present a multi-branch decision
network to minimize the intra-class distance of features. These lightweight
techniques bring zero extra time complexity but prominent detection accuracy
improvement. The proposed techniques can be equipped with any deep network
architectures and keep them trained end-to-end. Our SINet achieves
state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on
the KITTI benchmark and a new highway dataset, which contains a large variance
of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems
(T-ITS
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