This content is not included in
your SAE MOBILUS subscription, or you are not logged in.
Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm
Technical Paper
2017-01-0117
ISSN: 0148-7191, e-ISSN: 2688-3627
This content contains downloadable datasets
Annotation ability available
Sector:
Language:
English
Abstract
Accuracy in detecting a moving object is critical to autonomous driving or advanced driver assistance systems (ADAS). By including the object classification from multiple sensor detections, the model of the object or environment can be identified more accurately. The critical parameters involved in improving the accuracy are the size and the speed of the moving object. All sensor data are to be used in defining a composite object representation so that it could be used for the class information in the core object’s description. This composite data can then be used by a deep learning network for complete perception fusion in order to solve the detection and tracking of moving objects problem. Camera image data from subsequent frames along the time axis in conjunction with the speed and size of the object will further contribute in developing better recognition algorithms. In this paper, we present preliminary results using only camera images for detecting various objects using deep learning network, as a first step toward multi-sensor fusion algorithm development. The simulation experiments based on camera images show encouraging results where the proposed deep learning network based detection algorithm was able to detect various objects with certain degree of confidence. A laboratory experimental setup is being commissioned where three different types of sensors, a digital camera with 8 megapixel resolution, a LIDAR with 40m range, and ultrasonic distance transducer sensors will be used for multi-sensor fusion to identify the object in real-time.
Recommended Content
Technical Paper | ADAS Feature Concepts Development Framework via a Low Cost RC Car |
Technical Paper | Vision Based Object Distance Estimation |
Journal Article | GPS Modeling for Vehicle Intelligent Driving Simulation |
Authors
- Raja Sekhar Dheekonda - Indiana University - Purdue University Indianapolis
- Sampad Panda - Indiana University - Purdue University Indianapolis
- Md Nazmuzzaman khan - Indiana University - Purdue University Indianapolis
- Mohammad Hasan - Indiana University - Purdue University Indianapolis
- Sohel Anwar - Indiana University - Purdue University Indianapolis
Topic
Citation
Dheekonda, R., Panda, S., khan, M., Hasan, M. et al., "Object Detection from a Vehicle Using Deep Learning Network and Future Integration with Multi-Sensor Fusion Algorithm," SAE Technical Paper 2017-01-0117, 2017, https://doi.org/10.4271/2017-01-0117.Data Sets - Support Documents
Title | Description | Download |
---|---|---|
Unnamed Dataset 1 | ||
Unnamed Dataset 2 |
Also In
References
- Adams , J.L. Remote Control with Long Transmission Delays Stanford University 1961
- Dickmanns , E.D. Dynamic vision for perception and control of motion 2007 Springer Science & Business Media
- Braid , D. , Broggi A. , and Schmiedel G. The TerraMax autonomous vehicle concludes the 2005 DARPA grand challenge 2006 IEEE Intelligent Vehicles Symposium 2006 IEEE
- Montemerlo , M. , Winning the DARPA Grand Challenge with an AI robot Proceedings of the national conference on artificial intelligence 2006 Menlo Park, CA; Cambridge, MA; London AAAI Press; MIT Press 1999
- Funke , J. , Up to the limits: Autonomous Audi TTS Intelligent Vehicles Symposium (IV), 2012 IEEE 2012 IEEE
- Markoff , J. Google cars drive themselves, in traffic The New York Times 2010 10 A1 9
- Rauskolb , F.W. , Caroline: An autonomously driving vehicle for urban environments Journal of Field Robotics 2008 25 9 674 724
- Berlin , T. Spirit of berlin: An autonomous car for the DARPA urban challenge hardware and software architecture Jan 2007 5 2010
- Hall , D.L. and Garga A.K. Pitfalls in data fusion (and how to avoid them) Proceedings of the Second International Conference on Information Fusion (Fusion’99) 1999
- Baig , Q. Multisensor data fusion for detection and tracking of moving objects from a dynamic autonomous vehicle 2012 Ph. D. dissertation University of Grenoble1
- Chavez-Garcia , R.O. Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments 2014 Université de Grenoble
- Vu , T.-D. Vehicle perception: Localization, mapping with detection, classification and tracking of moving objects 2009 Institut National Polytechnique de Grenoble-INPG
- Baig , Q. , Fusion between laser and stereo vision data for moving objects tracking in intersection like scenario Intelligent Vehicles Symposium (IV), 2011 IEEE 2011 IEEE
- Grabe , B. , Ike T. , and Hoetter M. Evaluation method of grid based representation from sensor data Intelligent Vehicles Symposium, 2009 IEEE 2009 IEEE
- Subramanian , V. , Burks T. , and Dixon W. Sensor fusion using fuzzy logic enhanced kalman filter for autonomous vehicle guidance in citrus groves Transactions of the ASABE 2009 52 5 1411 1422
- Chavez-Garcia , R.O. and Aycard O. Multiple sensor fusion and classification for moving object detection and tracking IEEE Transactions on Intelligent Transportation Systems 2016 17 2 525 534
- karpathy Andrej , Toderici George , Large-Scale Video Classification with Convolutional Neural Networks CVPR '14 Proceedings, 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Lawrence S. , Face recognition: a convolutional neural-network approach IEEE Transactions on Neural Networks 2002
- Simonyan Karen , Zisserman Andrew Very Deep Convolutional Networks for Large-Scale Image Recognition ICLR 2015
- Erhan Dumitru , Scalable Object Detection using Deep Neuarl Networks The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014 2147 2154
- Ciregan Dan , Multi-column deep neural networks for image classification The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012
- Krizhevsky Alex , ImageNet Classification with Deep Convolutional Neural Networks Neural Information Processing Systems (NIPS) 2012
- Socher Richard , Convolutional-Recursive Deep Learning for 3D Object Classification Neural Information Processing Systems (NIPS) 2012
- Sermanet Pierre , Pedestrian Detection with Unsupervised Multi-stage Feature Learning the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012
- Viola P. and Jones M. J. Robust real-time face detection International Journal of Computer Vision 57 2 137 154 2004
- Leinhart R. and Maydt J. An extended set of haar-like features for rapid object detection Proceedings of the International Conference on Image Processing (ICIP '02) 1 I-900 I-903 IEEE Rochester, NY, USA September 2002
- Ahonen T. , Hadid A. , and Pietikäinen M. Face description with local binary patterns: application to face recognition IEEE Transactions on Pattern Analysis and Machine Intelligence 28 12 2037 2041 2006
- Hinton , G.E. Training products of experts by minimizing contrastive divergence Neural computation 14 8 1771 1800 2002
- Hinton , G. E. , Osindero , S. , and Teh , Y.-W. A fast learning algorithm for deep belief nets Neural computation 18 7 1527 1554 2006
- Vincent , P. , Larochelle , H. , Lajoie , I. , Bengio , Y. , and Manzagol , P.-A. 2010 Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion The Journal of Machine Learning Research 11 3371 3408
- Krizhevsky , A. , Sutskever , I. , and Hinton , G. E. 2012 Imagenet classification with deep convolutional neural networks Pereira , F. , Burges , C. , Bottou , L. , and Weinberger , K. Advances in Neural Information Processing Systems 25 1097 1105 Curran Associates, Inc.
- Socher Richard , Convolutional-Recursive Deep Learning for 3D Object Classification Neural Information Processing Systems (NIPS) 2012
- Sermanet Pierre , Pedestrian Detection with Unsupervised Multi-stage Feature Learning the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012
- Suraj Srinivas , Kiran Sarvadevabhatla Ravi , Reddy Mopuri Konda , Nikita Prabhu , Kruthiventi Srinivas S. S. , Venkatesh Babu R. 2016 A Taxonomy of Deep Convolutional Neural Nets for Computer Vision Frontiers in Robotics and AI 2 1
- Huval , B. , An Empirical Evaluation of Deep Learning on Highway Driving ARxiv.1504.01716v3 2015
- Angelova , A. Krizhevsky , A. Vanhoucke , V. Ogale , A. Ferguson D. Real-Time Pedestrian Detection With Deep Network Cascades Proc. of BMVC 2015