scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Vol 11 (7) ◽  
pp. 2925
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Path Planning ◽  
Object Detection ◽  
Short Term Memory ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Computational Time ◽  
End To End ◽  
On The Road

Autonomous vehicles are increasingly becoming a necessary trend towards building the smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection, amongst others. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that also considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper reviews the latest techniques towards creating our own end-to-end autonomous vehicle system, considering the state-of-the-art methods on object detection, and the possible incorporation of distributed systems and parallelization to deploy these methods. Our findings show that while techniques such as convolutional neural networks, recurrent neural networks, and long short-term memory can effectively handle the initial detection and path planning tasks, more efforts are required to implement cloud computing to reduce the computational time that these methods demand. Additionally, we have mapped different strategies to handle the parallelization task, both within and between the networks.

scholarly journals Object Detection, Distributed Cloud Computing and Parallelization Techniques for Autonomous Driving Systems

2021 ◽  
Author(s):  
Edgar Cortés Gallardo Medina ◽  
Victor Miguel Velazquez Espitia ◽  
Daniela Chípuli Silva ◽  
Sebastián Fernández Ruiz de las Cuevas ◽  
Marco Palacios Hirata ◽  
...  
Keyword(s):  
Path Planning ◽  
Object Detection ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
The Road ◽  
Vehicle System ◽  
End To End ◽  
On The Road ◽  
Distributed Cloud

Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

scholarly journals Scalable Object Detection for Edge Cloud Environments

2021 ◽  
Vol 3 ◽  
Author(s):  
Rory Hector ◽  
Muhammad Umar ◽  
Asif Mehmood ◽  
Zhu Li ◽  
Shuvra Bhattacharyya
Keyword(s):  
Neural Networks ◽  
Cloud Computing ◽  
Computational Complexity ◽  
Object Detection ◽  
Resource Constraints ◽  
Smart Cities ◽  
Detection Accuracy ◽  
Operational Conditions ◽  
Cloud Environments ◽  
Computing Platforms

Object detection is an important problem in a wide variety of computer vision applications for sustainable smart cities. Deep neural networks (DNNs) have attracted increasing interest in object detection due to their potential to provide high accuracy detection performance in challenging scenarios. However, DNNs involve high computational complexity and are therefore challenging to deploy under the tighter resource constraints of edge cloud environments compared to more resource-abundant platforms, such as conventional cloud computing platforms. Moreover, the monolithic structure of conventional DNN implementations limits their utility under the dynamically changing operational conditions that are typical in edge cloud computing. In this paper, we address these challenges and limitations of conventional DNN implementation techniques by introducing a new resource-adaptive scheme for DNN-based object detection. This scheme applies the recently-introduced concept of elastic neural networks, which involves the incorporation of multiple outputs within intermediate stages of the neural network backbone. We demonstrate a novel elastic DNN design for object detection, and we show how other methods for streamlining resource requirements, in particular network pruning, can be applied in conjunction with the proposed elastic network approach. Through extensive experiments, we demonstrate the ability of our methods to efficiently trade-off computational complexity and object detection accuracy for scalable deployment.

Convolutional Nonlinear Differential Recurrent Neural Networks for Crowd Scene Understanding

2018 ◽  
Vol 12 (04) ◽  
pp. 481-500 ◽  
Author(s):  
Naifan Zhuang ◽  
The Duc Kieu ◽  
Jun Ye ◽  
Kien A. Hua
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Scene Understanding ◽  
Image Data ◽  
High Density ◽  
Temporal Information ◽  
Deep Model ◽  
End To End ◽  
The Individual

With the growth of crowd phenomena in the real world, crowd scene understanding is becoming an important task in anomaly detection and public security. Visual ambiguities and occlusions, high density, low mobility, and scene semantics, however, make this problem a great challenge. In this paper, we propose an end-to-end deep architecture, convolutional nonlinear differential recurrent neural networks (CNDRNNs), for crowd scene understanding. CNDRNNs consist of GoogleNet Inception V3 convolutional neural networks (CNNs) and nonlinear differential recurrent neural networks (RNNs). Different from traditional non-end-to-end solutions which separate the steps of feature extraction and parameter learning, CNDRNN utilizes a unified deep model to optimize the parameters of CNN and RNN hand in hand. It thus has the potential of generating a more harmonious model. The proposed architecture takes sequential raw image data as input, and does not rely on tracklet or trajectory detection. It thus has clear advantages over the traditional flow-based and trajectory-based methods, especially in challenging crowd scenarios of high density and low mobility. Taking advantage of CNN and RNN, CNDRNN can effectively analyze the crowd semantics. Specifically, CNN is good at modeling the semantic crowd scene information. On the other hand, nonlinear differential RNN models the motion information. The individual and increasing orders of derivative of states (DoS) in differential RNN can progressively build up the ability of the long short-term memory (LSTM) gates to detect different levels of salient dynamical patterns in deeper stacked layers modeling higher orders of DoS. Lastly, existing LSTM-based crowd scene solutions explore deep temporal information and are claimed to be “deep in time.” Our proposed method CNDRNN, however, models the spatial and temporal information in a unified architecture and achieves “deep in space and time.” Extensive performance studies on the Violent-Flows, CUHK Crowd, and NUS-HGA datasets show that the proposed technique significantly outperforms state-of-the-art methods.

scholarly journals An Efficient Methodology for Object Classification Using Light Weight Deep Convolutional Neural Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 5965-5968
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Computational Cost ◽  
Computational Time ◽  
Video Frame ◽  
Surveillance Systems ◽  
Light Weight ◽  
Deep Convolutional Neural Networks ◽  
Deep Convolution Neural Network ◽  
Improving Accuracy

In current era, deep convolution neural networks (DCNNs) have good break-through in processing images while reducing computational cost and increasing accuracy. Proposed approach focuses on object detection using classification with DCNN model. This model uses feature map for pre-processing the images and convolution layers helps to minimize the processing using deep learning perceptron’s. After that the proposed approach uses Light – Weight Deep Convolution Neural Network(LW_DCNN) Model which includes less number of convolution layers, Max Pooling layers with relevant parameters and Dense, flatten layers to train the data using Leaky ReLU function for improving accuracy. The proposed methodology LW_DCNN is highly efficient compared to traditional classification techniques and presenting simple and powerful model for object detection in Video Surveillance Systems. This model also tested on GPU systems and proved efficiency in less computational time. Obtained Results are clearly shows that model is more efficient in classifying the objects intern classifying the working condition of the overhead power polls insulators in real time video frame sequences.

scholarly journals Target Binding and Sequence Prediction With LSTMs

2018 ◽  
Author(s):  
Michael Teti ◽  
Rachel StClair ◽  
Mirjana Pavlovic ◽  
Elan Barenholtz ◽  
William Hahn
Keyword(s):  
Neural Networks ◽  
Language Processing ◽  
Protein Function ◽  
Short Term Memory ◽  
Sequence Data ◽  
Computational Time ◽  
Literature Mining ◽  
Binding Motif ◽  
Sequential Data ◽  
Conserved Sequence

Deep recurrent neural networks (DRNNs) have recently demonstrated strong performance in sequential data analysis, such as natural language processing. These capabilities make them a promising tool for inferential analysis of sequentially structured bioinformatics data as well. Here, we assessed the ability of Long Short-Term Memory (LSTM) networks, a class of DRNNs, to predict properties of proteins based on their primary structures. The proposed architecture is trained and tested on two different datasets to predict whether a given sequence falls into a certain class or not. The first dataset, directly imported from Uniprot, was used to train the network on whether a given protein contained or did not contain a conserved sequence (homeodomain), and the second dataset, derived by literature mining, was used to train a network on whether a given protein binds or doesn't bind to Artemisinin, a drug typically used to treat malaria. In each case, the model was able to differentiate between the two different classes of sequences it was given with high accuracy, illustrating successful learning and generalization. Upon completion of training, an ROC curve was created using the homeodomain and artemisinin validation datasets. The AUC of these datasets was 0.80 and 0.87 respectively, further indicating the models' effectiveness. Furthermore, using these trained models, it was possible to derive a protocol for sequence detection of homeodomain and binding motif, which are well-documented in literature, and a known Artemisinin binding site, respectively [1-3]. Along with these contributions, we developed a python API to directly connect to Uniprot data sourcing, train deep neural networks on this primary sequence data using TensorFlow, and uniquely visualize the results of this analysis. Such an approach has the potential to drastically increase accuracy and reduce computational time and, current major limitations in informatics, from inquiry to discovery in protein function research

scholarly journals Comparison of different configuration space representations for path planning under combinatorial method

2019 ◽  
Vol 14 (1) ◽  
pp. 1 ◽  
Author(s):  
Sanjoy Kumar Debnath ◽  
Rosli Omar ◽  
Nor Badariyah Abdul Latip
Keyword(s):  
Path Planning ◽  
Configuration Space ◽  
Autonomous Vehicle ◽  
Computation Time ◽  
Computational Time ◽  
Space Representation ◽  
Human Beings ◽  
Combinatorial Method ◽  
Algorithm Configuration ◽  
Representation Techniques

<span>The use of autonomous vehicle/robot has been adopted widely to replace human beings in performing dangerous missions in adverse environments. Keeping this in mind, path planning ensures that the autonomous vehicle must safely arrive to its destination with required criteria like lower computation time, shortest travelled path and completeness. There are few kinds of path planning strategies, such as combinatorial method, sampling based method and bio-inspired method. Among them, combinatorial method can accomplish couple of criteria without further adjustment in conventional algorithm. Configuration space provides detailed information about the position of all points in the system and it is the space for all configurations. Therefore, C-space denotes the actual free space zone for the movement of robot and guarantees that the vehicle or robot must not collide with the obstacle. This paper analyses different C-Space representation techniques under combinatorial method based on the past researches and their findings with different criteria such as optimality, completeness, safety, memory uses, real time and computational time etc. Visibility Graph has optimality which is a unique from other</span>

scholarly journals Towards Enhanced Anomaly Object Detection and Face Recognition (EAODFR) in Surveillance Videos using Recurrent Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 3597-3603
Keyword(s):  
Neural Networks ◽  
Face Recognition ◽  
Object Detection ◽  
Recurrent Neural Networks ◽  
Short Term Memory ◽  
Region Of Interest ◽  
Surveillance Videos ◽  
Research Areas ◽  
Present Decade ◽  
Interest Detection

In the present decade, anomaly object detection and face recognition from surveillance videos from diverse environments have become interesting and challenging research areas in computer vision. This paper works on developing an Enhanced Anomaly Object Detection and Face Recognition (EAODFR) model using Recurrent Neural Networks (RNN). Moreover, fractional derivative based background separation has been incorporated for framing efficient background subtraction model and foreground segmentation with appropriate pixel definitions on each frame of the surveillance videos. The Region of Interest detection has been done using optimal thresholding and for detecting anomaly objects. Further, efficient face recognition has been accomplished by designing the Recurrent Neural Networks (RNN), which is implemented with Long Short-Term Memory (LSTM). The recurrent NN are trained in terms of determining anomalous objects using the extracted features in the each frame of the video. The obtained results are analyzed in terms of precision, recall and f-measure and compared with some existing face recognition models. The comparative analysis provides better results and outperforms others.

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