8 research outputs found
Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently
Existing hand detection methods usually follow the pipeline of multiple
stages with high computation cost, i.e., feature extraction, region proposal,
bounding box regression, and additional layers for rotated region detection. In
this paper, we propose a new Scale Invariant Fully Convolutional Network
(SIFCN) trained in an end-to-end fashion to detect hands efficiently.
Specifically, we merge the feature maps from high to low layers in an iterative
way, which handles different scales of hands better with less time overhead
comparing to concatenating them simply. Moreover, we develop the Complementary
Weighted Fusion (CWF) block to make full use of the distinctive features among
multiple layers to achieve scale invariance. To deal with rotated hand
detection, we present the rotation map to get rid of complex rotation and
derotation layers. Besides, we design the multi-scale loss scheme to accelerate
the training process significantly by adding supervision to the intermediate
layers of the network. Compared with the state-of-the-art methods, our
algorithm shows comparable accuracy and runs a 4.23 times faster speed on the
VIVA dataset and achieves better average precision on Oxford hand detection
dataset at a speed of 62.5 fps.Comment: Accepted to AAAI201
DMD: A Large-Scale Multi-Modal Driver Monitoring Dataset for Attention and Alertness Analysis
Vision is the richest and most cost-effective technology for Driver
Monitoring Systems (DMS), especially after the recent success of Deep Learning
(DL) methods. The lack of sufficiently large and comprehensive datasets is
currently a bottleneck for the progress of DMS development, crucial for the
transition of automated driving from SAE Level-2 to SAE Level-3. In this paper,
we introduce the Driver Monitoring Dataset (DMD), an extensive dataset which
includes real and simulated driving scenarios: distraction, gaze allocation,
drowsiness, hands-wheel interaction and context data, in 41 hours of RGB, depth
and IR videos from 3 cameras capturing face, body and hands of 37 drivers. A
comparison with existing similar datasets is included, which shows the DMD is
more extensive, diverse, and multi-purpose. The usage of the DMD is illustrated
by extracting a subset of it, the dBehaviourMD dataset, containing 13
distraction activities, prepared to be used in DL training processes.
Furthermore, we propose a robust and real-time driver behaviour recognition
system targeting a real-world application that can run on cost-efficient
CPU-only platforms, based on the dBehaviourMD. Its performance is evaluated
with different types of fusion strategies, which all reach enhanced accuracy
still providing real-time response.Comment: Accepted to ECCV 2020 workshop - Assistive Computer Vision and
Robotic
A Context Aware Classification System for Monitoring Driver’s Distraction Levels
Understanding the safety measures regarding developing self-driving futuristic cars is a concern for decision-makers, civil society, consumer groups, and manufacturers. The researchers are trying to thoroughly test and simulate various driving contexts to make these cars fully secure for road users. Including the vehicle’ surroundings offer an ideal way to monitor context-aware situations and incorporate the various hazards. In this regard, different studies have analysed drivers’ behaviour under different case scenarios and scrutinised the external environment to obtain a holistic view of vehicles and the environment. Studies showed that the primary cause of road accidents is driver distraction, and there is a thin line that separates the transition from careless to dangerous. While there has been a significant improvement in advanced driver assistance systems, the current measures neither detect the severity of the distraction levels nor the context-aware, which can aid in preventing accidents. Also, no compact study provides a complete model for transitioning control from the driver to the vehicle when a high degree of distraction is detected.
The current study proposes a context-aware severity model to detect safety issues related to driver’s distractions, considering the physiological attributes, the activities, and context-aware situations such as environment and vehicle. Thereby, a novel three-phase Fast Recurrent Convolutional Neural Network (Fast-RCNN) architecture addresses the physiological attributes. Secondly, a novel two-tier FRCNN-LSTM framework is devised to classify the severity of driver distraction. Thirdly, a Dynamic Bayesian Network (DBN) for the prediction of driver distraction. The study further proposes the Multiclass Driver Distraction Risk Assessment (MDDRA) model, which can be adopted in a context-aware driving distraction scenario. Finally, a 3-way hybrid CNN-DBN-LSTM multiclass degree of driver distraction according to severity level is developed. In addition, a Hidden Markov Driver Distraction Severity Model (HMDDSM) for the transitioning of control from the driver to the vehicle when a high degree of distraction is detected.
This work tests and evaluates the proposed models using the multi-view TeleFOT naturalistic driving study data and the American University of Cairo dataset (AUCD). The evaluation of the developed models was performed using cross-correlation, hybrid cross-correlations, K-Folds validation. The results show that the technique effectively learns and adopts safety measures related to the severity of driver distraction. In addition, the results also show that while a driver is in a dangerous distraction state, the control can be shifted from driver to vehicle in a systematic manner