20 research outputs found
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
Combined Learned and Classical Methods for Real-Time Visual Perception in Autonomous Driving
Autonomy, robotics, and Artificial Intelligence (AI) are among the main defining themes of next-generation societies. Of the most important applications of said technologies is driving automation which spans from different Advanced Driver Assistance Systems (ADAS) to full self-driving vehicles. Driving automation is promising to reduce accidents, increase safety, and increase access to mobility for more people such as the elderly and the handicapped. However, one of the main challenges facing autonomous vehicles is robust perception which can enable safe interaction and decision making. With so many sensors to perceive the environment, each with its own capabilities and limitations, vision is by far one of the main sensing modalities. Cameras are cheap and can provide rich information of the observed scene. Therefore, this dissertation develops a set of visual perception algorithms with a focus on autonomous driving as the target application area. This dissertation starts by addressing the problem of real-time motion estimation of an agent using only the visual input from a camera attached to it, a problem known as visual odometry. The visual odometry algorithm can achieve low drift rates over long-traveled distances. This is made possible through the innovative local mapping approach used. This visual odometry algorithm was then combined with my multi-object detection and tracking system. The tracking system operates in a tracking-by-detection paradigm where an object detector based on convolution neural networks (CNNs) is used. Therefore, the combined system can detect and track other traffic participants both in image domain and in 3D world frame while simultaneously estimating vehicle motion. This is a necessary requirement for obstacle avoidance and safe navigation. Finally, the operational range of traditional monocular cameras was expanded with the capability to infer depth and thus replace stereo and RGB-D cameras. This is accomplished through a single-stream convolution neural network which can output both depth prediction and semantic segmentation. Semantic segmentation is the process of classifying each pixel in an image and is an important step toward scene understanding. Literature survey, algorithms descriptions, and comprehensive evaluations on real-world datasets are presented.Ph.D.College of Engineering & Computer ScienceUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/153989/1/Mohamed Aladem Final Dissertation.pdfDescription of Mohamed Aladem Final Dissertation.pdf : Dissertatio