67,425 research outputs found
Low-power dynamic object detection and classification with freely moving event cameras
We present the first purely event-based, energy-efficient approach for dynamic object detection and categorization with a freely moving event camera. Compared to traditional cameras, event-based object recognition systems are considerably behind in terms of accuracy and algorithmic maturity. To this end, this paper presents an event-based feature extraction method devised by accumulating local activity across the image frame and then applying principal component analysis (PCA) to the normalized neighborhood region. Subsequently, we propose a backtracking-free k-d tree mechanism for efficient feature matching by taking advantage of the low-dimensionality of the feature representation. Additionally, the proposed k-d tree mechanism allows for feature selection to obtain a lower-dimensional object representation when hardware resources are limited to implement PCA. Consequently, the proposed system can be realized on a field-programmable gate array (FPGA) device leading to high performance over resource ratio. The proposed system is tested on real-world event-based datasets for object categorization, showing superior classification performance compared to state-of-the-art algorithms. Additionally, we verified the real-time FPGA performance of the proposed object detection method, trained with limited data as opposed to deep learning methods, under a closed-loop aerial vehicle flight mode. We also compare the proposed object categorization framework to pre-trained convolutional neural networks using transfer learning and highlight the drawbacks of using frame-based sensors under dynamic camera motion. Finally, we provide critical insights about the feature extraction method and the classification parameters on the system performance, which aids in understanding the framework to suit various low-power (less than a few watts) application scenarios
Tree-based Intelligent Intrusion Detection System in Internet of Vehicles
The use of autonomous vehicles (AVs) is a promising technology in Intelligent
Transportation Systems (ITSs) to improve safety and driving efficiency.
Vehicle-to-everything (V2X) technology enables communication among vehicles and
other infrastructures. However, AVs and Internet of Vehicles (IoV) are
vulnerable to different types of cyber-attacks such as denial of service,
spoofing, and sniffing attacks. In this paper, an intelligent intrusion
detection system (IDS) is proposed based on tree-structure machine learning
models. The results from the implementation of the proposed intrusion detection
system on standard data sets indicate that the system has the ability to
identify various cyber-attacks in the AV networks. Furthermore, the proposed
ensemble learning and feature selection approaches enable the proposed system
to achieve high detection rate and low computational cost simultaneously.Comment: Accepted in IEEE Global Communications Conference (GLOBECOM) 201
Detecting Distracted Driving with Deep Learning
© Springer International Publishing AG 2017Driver distraction is the leading factor in most car crashes and near-crashes. This paper discusses the types, causes and impacts of distracted driving. A deep learning approach is then presented for the detection of such driving behaviors using images of the driver, where an enhancement has been made to a standard convolutional neural network (CNN). Experimental results on Kaggle challenge dataset have confirmed the capability of a convolutional neural network (CNN) in this complicated computer vision task and illustrated the contribution of the CNN enhancement to a better pattern recognition accuracy.Peer reviewe
Network Uncertainty Informed Semantic Feature Selection for Visual SLAM
In order to facilitate long-term localization using a visual simultaneous
localization and mapping (SLAM) algorithm, careful feature selection can help
ensure that reference points persist over long durations and the runtime and
storage complexity of the algorithm remain consistent. We present SIVO
(Semantically Informed Visual Odometry and Mapping), a novel
information-theoretic feature selection method for visual SLAM which
incorporates semantic segmentation and neural network uncertainty into the
feature selection pipeline. Our algorithm selects points which provide the
highest reduction in Shannon entropy between the entropy of the current state
and the joint entropy of the state, given the addition of the new feature with
the classification entropy of the feature from a Bayesian neural network. Each
selected feature significantly reduces the uncertainty of the vehicle state and
has been detected to be a static object (building, traffic sign, etc.)
repeatedly with a high confidence. This selection strategy generates a sparse
map which can facilitate long-term localization. The KITTI odometry dataset is
used to evaluate our method, and we also compare our results against ORB_SLAM2.
Overall, SIVO performs comparably to the baseline method while reducing the map
size by almost 70%.Comment: Published in: 2019 16th Conference on Computer and Robot Vision (CRV
Unmanned Aerial Systems for Wildland and Forest Fires
Wildfires represent an important natural risk causing economic losses, human
death and important environmental damage. In recent years, we witness an
increase in fire intensity and frequency. Research has been conducted towards
the development of dedicated solutions for wildland and forest fire assistance
and fighting. Systems were proposed for the remote detection and tracking of
fires. These systems have shown improvements in the area of efficient data
collection and fire characterization within small scale environments. However,
wildfires cover large areas making some of the proposed ground-based systems
unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial
Systems (UAS) were proposed. UAS have proven to be useful due to their
maneuverability, allowing for the implementation of remote sensing, allocation
strategies and task planning. They can provide a low-cost alternative for the
prevention, detection and real-time support of firefighting. In this paper we
review previous work related to the use of UAS in wildfires. Onboard sensor
instruments, fire perception algorithms and coordination strategies are
considered. In addition, we present some of the recent frameworks proposing the
use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more
efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at:
https://doi.org/10.3390/drones501001
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