A DEEP LEARNING APPROACH FOR THE ANALYSIS AND DETECTION OF OBJECT IN VIDEO FRAMES USING YOLO FASTER RCNN
Abstract
Object detection often refers to a collection of generic computer vision tasks which potentially identifies objects from the given video inputs. As object detection combines two main tasks like image classification and object localisation which eventually identifies one or more objects in a specified image frame. The space in which this research is very popular is one where researchers continue developing new aspects in detecting objects, and in various areas including autonomous driving, health-care monitoring, anomaly detection etc. Traditional object detection is done using shallow features and handcrafted architecture which eventually doesn’t give effective results. So, to overcome this, the use of advanced technology such as Deep learning comes into play as it has a wide hand in this field. Thereby this paper brings an effective object detection model from video frames in which initially a)Data Collection from ImageNet VID and CIFAR-10 video analysis b) Feature extraction using a convolutional autoencoder c) feature selection using SE-block d) Classification using integration of Yolo-Faster-RCNN. The study shows that the proposed method outperforms with 95% accuracy when compared with other state-of-art models.
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[2]. Maggipinto, M., Masiero, C., Beghi, A., & Susto, G. A. (2018). A convolutional autoencoder approach for feature extraction in virtual metrology. Procedia Manufacturing, 17, 126-133.
[3]. Naikal, Nikhil, Allen Y. Yang, and S. Shankar Sastry. "Informative feature selection for object recognition via sparse PCA." In 2011 International Conference on Computer Vision, pp. 818-825. IEEE, 2011.
[4]. Hu, Jie, Li Shen, and Gang Sun. "Squeeze-and-excitation networks." In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132-7141. 2018.
[5]. Sun, Qiaoqiao, Xuefeng Liu, and Salah Bourennane. "Unsupervised Multi-Level Feature Extraction for Improvement of Hyperspectral Classification." Remote Sensing 13.8 (2021): 1602.
[6]. Ren, Shaoqing, et al. "Faster R-CNN: towards real-time object detection with region proposal networks." IEEE transactions on pattern analysis and machine intelligence 39.6 (2016): 1137-1149.
[7]. Oliveira, Inês, Nuno Correia, and Nuno Guimarães. "Image processing techniques for video content extraction." In Proceedings of 4th Dellos Workshop. 1997.
[8]. Shahriar, Md Tanzil, and Huyue Li. "A Study of Image Pre-processing for Faster Object Recognition." arXiv preprint arXiv:2011.06928 (2020).
[9]. Raj, Jeberson Retna, and Senduru Srinivasulu. "Object Detection in Live Streaming Video Using Deep Learning Approach." In IOP Conference Series: Materials Science and Engineering, vol. 1020, no. 1, p. 012028. IOP Publishing, 2021.
[10]. Russakovsky, Olga, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang et al. "Imagenet large scale visual recognition challenge." International journal of computer vision 115, no. 3 (2015): 211-252.
[11]. Kalaivani, R., and C. Manicha. "Object Detection in Video Frames Using Various Approaches." International Journal of Advanced Research in Computer and Communication Engineering 2, no. 9 (2013): 157-160.
[12]. Patro, Shrikant Jagannath, and V. M. Nisha. "Real-Time Video Analytics for Object Detection and Face Identification using Deep Learning."
[13]. Ravish Aradhya, H. V. "Object detection and tracking using deep learning and artificial intelligence for video surveillance applications." International Journal of Advanced Computer Science and Applications 10, no. 12 (2019): 517-530.
[14]. Ma, Yongjun, and Songhua Zhang. "Feature Selection Module for CNN Based Object Detector." IEEE Access 9 (2021): 69456-69466.
[15]. Ploeger, Spencer, and Lucas Dasovic. "Issues in Object Detection in Videos using Common Single-Image CNNs." arXiv preprint arXiv:2105.12822 (2021).
[16]. Pal, Sankar K., Anima Pramanik, Jhareswar Maiti, and Pabitra Mitra. "Deep learning in multi-object detection and tracking: state of the art." Applied Intelligence 51, no. 9 (2021): 6400-6429.
[17]. Zhao, Zhong-Qiu, Peng Zheng, Shou-tao Xu, and Xindong Wu. "Object detection with deep learning: A review." IEEE transactions on neural networks and learning systems 30, no. 11 (2019): 3212-3232.
[18]. T. Lu et al., Video Text Detection, Advances in Computer Vision and Pattern Recognition, Springer-Verlag London 2014.
[19]. Chandan, G., Ayush Jain, and Harsh Jain. "Real-time object detection and tracking using Deep Learning and OpenCV." 2018 international conference on inventive research in computing applications (ICIRCA). IEEE, 2018
[20]. Lu, Shengyu, et al. "A real-time object detection algorithm for video." Computers & Electrical Engineering 77 (2019): 398-408.
[21]. Göring, Christoph, Erik Rodner, Alexander Freytag, and Joachim Denzler. 2014. “Nonparametric Part Transfer for Fine-Grained
[22]. Girshick, Ross, Forrest Iandola, Trevor Darrell, Tian, Yonglong, Ping Luo, Xiaogang Wang, and Xiaoou Tang. (2015). “Deep Learning Strong Parts for Pedestrian Detection.” In Proceedings of the IEEE International Conference on Computer Vision, 1904–12
[23]. Zhang, Ning, Jeff Donahue, Ross Girshick, and Trevor Darrell. (2014). “Part-Based R-CNNs for Fine-Grained Category Detection.” In European Conference on Computer Vision, 834–49
[24]. Redmon, J, and A Farhadi. (2017). “YOLO9000: Better, Faster, Stronger.” In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 6517–25.
[25]. Shih, Ya-Fang et al. (2017). “Deep Co-Occurrence Feature Learning for Visual Object Recognition.” In Proc. Conf. Computer Vision and Pattern Recognition
[26]. Faisal I.Basshir et.al “Real-time motion trajectory-based indexing and retrieval of video sequence” IEEE, pp: 1-8, January 7, 2005
[27]. Sanjirani shantaiya et.al, “ A survey on approaches of object detection”, International journal of computer applications, Vol-65, No-18, March 2013
[28]. Lakshmi Rupa G, Gitanjali, “A video mining application for image retrieval”, International journal of computer applications, Vol-20, No-3, pp46-52, April 2011.
[29]. Oh, S. Kang, H,” Object detection and classification by decision-level fusion for intelligent vehicle systems”. Sensors,2017
[30]. A. Sanin, C. Sanderson, B. C. Lovell, "Shadow detection: A survey and comparative evaluation of recent methods", Pattern Recognit., vol. 45, no. 4, pp. 1684-1695, 2012.
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