scholarly journals A Lightweight Motional Object Behavior Prediction System Harnessing Deep Learning Technology for Embedded ADAS Applications

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 692
Author(s):  
Wen-Chia Tsai ◽  
Jhih-Sheng Lai ◽  
Kuan-Chou Chen ◽  
Vinay M.Shivanna ◽  
Jiun-In Guo
Keyword(s):  
Embedded System ◽  
Moving Objects ◽  
Object Localization ◽  
Crossing Over ◽  
Learning Technology ◽  
Prediction System ◽  
Behavior Prediction ◽  
Detection Model ◽  
Pedestrian Crossing ◽  
Object Behavior

This paper proposes a lightweight moving object prediction system to detect and recognize pedestrian crossings, vehicles cutting-in, and vehicles ahead applying emergency brakes based on a 3D Convolution network for behavior prediction. The proposed design significantly improves the performance of the conventional 3D convolution network (C3D) adapted to predict the behaviors employing behavior recognition network capable of performing object localization, which is pivotal in detecting the numerous moving objects’ behaviors, combining and verifying the detected objects with the results of the YOLO v3 detection model with that of the proposed C3D model. Since the proposed system is a lightweight CNN model requiring far lesser parameters, it can be efficiently realized on an embedded system for real-time applications. The proposed lightweight C3D model achieves 10 frames per second (FPS) on a NVIDIA Jetson AGX Xavier and yields over 92.8% accuracy in recognizing pedestrian crossing, over 94.3% accuracy in detecting vehicle cutting-in behavior, and over 95% accuracy for vehicles applying emergency brakes.

scholarly journals Analysis and Prediction of Pedestrians’ Violation Behavior at the Intersection Based on a Markov Chain

2021 ◽  
Vol 13 (10) ◽  
pp. 5690
Author(s):  
Chengyuan Mao ◽  
Lewen Bao ◽  
Shengde Yang ◽  
Wenjiao Xu ◽  
Qin Wang
Keyword(s):  
Markov Chain ◽  
Markov Chain Model ◽  
Warning System ◽  
Demographic Characteristics ◽  
Chain Model ◽  
Behavior Prediction ◽  
Road Geometry ◽  
Using Data ◽  
Pedestrian Crossing ◽  
Prediction Approach

Pedestrian violations pose a danger to themselves and other road users. Most previous studies predict pedestrian violation behaviors based only on pedestrians’ demographic characteristics. In practice, in addition to demographic characteristics, other factors may also impact pedestrian violation behaviors. Therefore, this study aims to predict pedestrian crossing violations based on pedestrian attributes, traffic conditions, road geometry, and environmental conditions. Data on the pedestrian crossing, both in compliance and in violation, were collected from 10 signalized intersections in the city of Jinhua, China. We propose an illegal pedestrian crossing behavior prediction approach that consists of a logistic regression model and a Markov Chain model. The former calculates the likelihood that the first pedestrian who decides to cross the intersection illegally within each signal cycle, while the latter computes the probability that the subsequent pedestrians who decides to follow the violation. The proposed approach was validated using data gathered from an additional signalized intersection in Jinhua city. The results show that the proposed approach has a robust ability in pedestrian violation behavior prediction. The findings can provide theoretical references for pedestrian signal timing, crossing facility optimization, and warning system design.

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

scholarly journals Life-Stage Modeling by Customer-Manifold Embedding

2017 ◽  
Author(s):  
Jing-Wen Yang ◽  
Yang Yu ◽  
Xiao-Peng Zhang
Keyword(s):  
Online Shopping ◽  
Life Stage ◽  
Customer Behavior ◽  
Behavior Patterns ◽  
Prediction System ◽  
Behavior Prediction ◽  
Real World Data ◽  
Manifold Embedding ◽  
Latent Space ◽  
Hidden Layer

A person experiences different stages throughout the life, causing dramatically varying behavior patterns. In applications such as online-shopping, it has been observed that customer behaviors are largely affected by their stages and are evolving over time. Although this phenomena has been recognized previously, very few studies tried to model the life-stage and make use of it. In this paper, we propose to discover a latent space, called customer-manifold, on which a position corresponds to a customer stage. The customer-manifold allows us to train a static prediction model that captures dynamic customer behavior patterns. We further embed the learned customer-manifold into a neural network model as a hidden layer output, resulting in an efficient and accurate customer behavior prediction system. We apply this system to online-shopping recommendation. Experiments in real world data show that taking customer-manifold into account can improve the performance of the recommender system. Moreover, visualization of the customer-manifold space may also be helpful to understand the evolutionary customer behaviors.

Development of a Faster R-CNN-based Marine Debris Detection Model for an Embedded System

2021 ◽  
Vol 27 (12) ◽  
pp. 1038-1043
Author(s):  
Younghun Byeon ◽  
Eunju Kim ◽  
Hyeon Jun Lim ◽  
Han Sol Kim
Keyword(s):  
Embedded System ◽  
Marine Debris ◽  
Detection Model

scholarly journals Smoke Object Segmentation and the Dynamic Growth Feature Model for Video-Based Smoke Detection Systems

Symmetry ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 1075
Author(s):  
Md Rashedul Islam ◽  
Md Amiruzzaman ◽  
Shahriar Nasim ◽  
Jungpil Shin
Keyword(s):  
Growth Analysis ◽  
Moving Objects ◽  
Feature Model ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Smoke Detection ◽  
Detection Model ◽  
Detection Systems ◽  
Feature Based ◽  
Growth Feature

This article concerns smoke detection in the early stages of a fire. Using the computer-aided system, the efficient and early detection of smoke may stop a massive fire incident. Without considering the multiple moving objects on background and smoke particles analysis (i.e., pattern recognition), smoke detection models show suboptimal performance. To address this, this paper proposes a hybrid smoke segmentation and an efficient symmetrical simulation model of dynamic smoke to extract a smoke growth feature based on temporal frames from a video. In this model, smoke is segmented from the multi-moving object on the complex background using the Gaussian’s Mixture Model (GMM) and HSV (hue-saturation-value) color segmentation to encounter the candidate smoke and non-smoke regions in the preprocessing stage. The preprocessed temporal frames with moving smoke are analyzed by the dynamic smoke growth analysis and spatial-temporal frame energy feature extraction model. In dynamic smoke growth analysis, the temporal frames are segmented in blocks and the smoke growth representations are formulated from corresponding blocks. Finally, the classifier was trained using the extracted features to classify and detect smoke using a Radial Basis Function (RBF) non-linear Gaussian kernel-based binary Support Vector Machine (SVM). For validating the proposed smoke detection model, multi-conditional video clips are used. The experimental results suggest that the proposed model outperforms state-of-the-art algorithms.

scholarly journals Providing Email Privacy by Preventing Webmail from Loading Malicious XSS Payloads

2020 ◽  
Vol 10 (13) ◽  
pp. 4425
Author(s):  
Yong Fang ◽  
Yijia Xu ◽  
Peng Jia ◽  
Cheng Huang
Keyword(s):  
Modern Society ◽  
Attention Mechanism ◽  
Internet Technology ◽  
Learning Technology ◽  
Personal Privacy ◽  
Detection Model ◽  
Good Target ◽  
Communication Method ◽  
Field Information ◽  
Formal Communication

With the development of internet technology, email has become the formal communication method in modern society. Email often contains a large amount of personal privacy information, possible business agreements, and sensitive attachments, which make emails a good target for hackers. One of the most common attack method used by hackers is email XSS (Cross-site scripting). Through exploiting XSS vulnerabilities, hackers can steal identities, logging into the victim’s mailbox and stealing content directly. Therefore, this paper proposes an email XSS detection model based on deep learning technology, which can identify whether the XSS payload is carried in the email or not. Firstly, the model could extract the Sender, Receiver, Subject, Content, Attachment field information from the original email. Secondly, the email XSS corpus is formed after data processing. The Word2Vec algorithm is introduced to train the corpus and extract features for each email sample. Finally, the model uses the Bidirectional-RNN algorithm and Attention mechanism to train the email XSS detection model. In the experiment, the AUC (area under curve) value of the Bidirectional-RNN model reached 0.9979. When the Attention mechanism was added, the accuracy upper limit of the Bidirectional-RNN model was raised to 0.9936, and the loss value was reduced to 0.03.

scholarly journals Coupling Intent and Action for Pedestrian Crossing Behavior Prediction

2021 ◽  
Author(s):  
Yu Yao ◽  
Ella Atkins ◽  
Matthew Johnson-Roberson ◽  
Ram Vasudevan ◽  
Xiaoxiao Du
Keyword(s):  
Traffic Safety ◽  
Autonomous Vehicles ◽  
State Of The Art ◽  
Semantic Interpretation ◽  
Behavior Prediction ◽  
Probabilistic Representation ◽  
Class Actions ◽  
The Future ◽  
Naturalistic Driving ◽  
Pedestrian Crossing

Accurate prediction of pedestrian crossing behaviors by autonomous vehicles can significantly improve traffic safety. Existing approaches often model pedestrian behaviors using trajectories or poses but do not offer a deeper semantic interpretation of a person's actions or how actions influence a pedestrian's intention to cross in the future. In this work, we follow the neuroscience and psychological literature to define pedestrian crossing behavior as a combination of an unobserved inner will (a probabilistic representation of binary intent of crossing vs. not crossing) and a set of multi-class actions (e.g., walking, standing, etc.). Intent generates actions, and the future actions in turn reflect the intent. We present a novel multi-task network that predicts future pedestrian actions and uses predicted future action as a prior to detect the present intent and action of the pedestrian. We also designed an attention relation network to incorporate external environmental contexts thus further improve intent and action detection performance. We evaluated our approach on two naturalistic driving datasets, PIE and JAAD, and extensive experiments show significantly improved and more explainable results for both intent detection and action prediction over state-of-the-art approaches. Our code is available at: https://github.com/umautobots/pedestrian_intent_action_detection

A Note on Fuelbeds and Fire Behavior in Alang-Alang (Imperata Cylindrica)

1992 ◽  
Vol 2 (1) ◽  
pp. 41 ◽  
Author(s):  
S Pickford ◽  
M Suharti ◽  
A Wibowo
Keyword(s):  
Relative Humidity ◽  
Fire Behavior ◽  
Acacia Mangium ◽  
Imperata Cylindrica ◽  
High Relative Humidity ◽  
Prediction System ◽  
Behavior Prediction ◽  
Spread Rate ◽  
Physical Evidence ◽  
Made In

Fire behavior on a 2 ha fire, inferred from physical evidence observed one week after the fire, was compared with fire behavior estimates obtained using the BEHAVE fire behavior prediction system and fuel measurements in Imperata cylindrica (Alang-alang) made in the same area. This fire probably burned under light winds (3-5 km), high relative humidity, and spread slowly with moderate flame lengths (approximately 100 m hr-1 spread rate, 0.5 - 0.7 m flame lengths). Although appar- ently killed by lethal crown and bole scorch, the young Acacia mangium overstory through which the fire burned resprouted vigorously and apparently survived.

Modelling logic and the Canadian Forest Fire Behavior Prediction System

1998 ◽  
Vol 74 (1) ◽  
pp. 50-52 ◽  
Author(s):  
C. E. Van Wagner
Keyword(s):  
Forest Fire ◽  
Fire Behavior ◽  
Fire Spread ◽  
Fire Research ◽  
Fire Intensity ◽  
Prediction System ◽  
Behavior Prediction ◽  
Crown Fire ◽  
Fire Modelling ◽  
Semi Empirical

This article outlines the flexible semi-empirical philosophy used throughout six decades of fire research by the Canadian Forest Service, culminating in the development of the Forest Fire Behavior Prediction System. It then describes the principles involved when spread rate and fuel consumption are estimated separately to yield fire intensity, and the anomaly that has resulted from the omission of a foliar-moisture effect on crown-fire spread. Judged on its results so far, this Canadian approach has held its own against any other, and holds full promise for the future as well. Key words: forest fire behavior, Canadian FBP System, fire modelling, crown-fire theory, fire research philosophy

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