Object detection, localization and tracking using Single Shot Detector (SSD)

G. Vishnu Vardhan Rao; S Vamsi Kumar Reddy; Shaik Ansar Basha; Gaddam Bharath

doi:10.53730/ijhs.v6nS1.6904

Object detection, localization and tracking using Single Shot Detector (SSD)

https://doi.org/10.53730/ijhs.v6nS1.6904

Authors

G.Vishnu Vardhan Rao Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India
S Vamsi Kumar Reddy Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India
Shaik Ansar Basha Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India
Gaddam Bharath Department of Electronics and Communication Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai, India

Keywords:

Object Detection, Object Localization, Object Classification, Convolutional Neural Networks

Abstract

We are trying to Detect, Localize and classify objects in an image using neural networks. We are using Single Shot detector for performing the task. The output of Single Shot Detector is Bounding boxes. It will try to localize the objects in an image and also tell which object is present in an image. The network predict probability scores of all the classes, we will take the highest probability of all the predicted probabilities. The network also predicts bounding boxes in an image, which can be reduced using NonMax Suppression. Object Detection is most widely used task in computer vision. We can locate objects based on our interest. Object detection is most widely used in real time situations and it has many practical applications. Some of the applications include Vehicle number plate detection, face mask detection, face recognition. Applications include face detection, pedestrian detection and vehicle detection. Single Shot Detector is Deep Learning based algorithm.

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References

Uijlings, J.R., van de Sande, K.E., Gevers, T., Smeulders, A.W.: Selective search for object recognition. IJCV (2013)

Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. In: NIPS. (2015)

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR. (2016)

Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., LeCun, Y.: Overfeat: Integrated recognition, localization and detection using convolutional networks. In: ICLR. (2014)

Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR. (2016)

Girshick, R.: Fast R-CNN. In: ICCV. (2015)

Erhan, D., Szegedy, C., Toshev, A., Anguelov, D.: Scalable object detection using deep neural networks. In: CVPR. (2014)

Szegedy, C., Reed, S., Erhan, D., Anguelov, D.: Scalable, high-quality object detection. arXiv preprint arXiv:1412.1441 v3 (2015)

He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: ECCV. (2014)

V. Prabhu1and G. Gunasekaran2 ,Fuzzy Logic based NAM Speech Recognition for Tamil Syllables,Indian Journal of Science and Technology, Vol 9(1),DOI: 10.17485/ijst/2016/v9i1/85763, ISSN (Online) :0974-5645.January 2016.

Hariharan, B., Arbelaez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation ´ and fine-grained localization. In: CVPR. (2015)

Liu, W., Rabinovich, A., Berg, A.C.: ParseNet: Looking wider to see better. In: ILCR. (2016)

Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Object detectors emerge in deep scene cnns. In: ICLR. (2015)

Prabhu.V.,Kuppusamy, P.G., Karthikeyan, A.,R. Varatharajan. (2019) Evaluation and analysis of data driven in expectation maximization segmentation through various initialization techniques in medical images, Multimedia Tools and Application, Springer , Vol 77, Issue 8, pp 10375–10390.

Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: NIPS. (2015)

Published

03-05-2022

How to Cite

Rao, G. V. V., Reddy, S. V. K., Basha, S. A., & Bharath, G. (2022). Object detection, localization and tracking using Single Shot Detector (SSD). International Journal of Health Sciences, 6(S1), 7306–7319. https://doi.org/10.53730/ijhs.v6nS1.6904

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Issue

Vol. 6 No. S1 (2022)

Section

Peer Review Articles

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