© 2019, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 2725 Object Detection in Real Time using AI and Deep Learning Piyush Sanjay Zope 1 , Suhas Khatal 2 , Nishikant Bahalkar 3 , Rushikesh Gontlewar 4 , Digambar Jadhav 5 1 Piyush Sanjay Zope, Dept. of Computer Engineering, D Y Patil Institute of Technology, Pimpri, Maharashtra, India 2 Suhas Khatal, Dept. of Computer Engineering, D Y Patil Institute of Technology, Pimpri, Maharashtra, India 3 Nishikant Bahalkar, Dept. of Computer Engineering, D Y Patil Institute of Technology, Pimpri, Maharashtra, India 4 Rushikesh Gontlewar, Dept. of Computer Engineering, D Y Patil Institute of Technology, Pimpri, Maharashtra, India 5 Prof. Digambar Jadhav, Dept. of Computer Engineering, D Y Patil Institute of Technology, Pimpri, Maharashtra, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - In object detection it is very challenging task to track movements of objects and generate high efficiency results. Our main focus area of object detection is to enhance the E- commerce business and to provide a better experience to customer. We are trying to make a model which will be beneficial for user as well as provider. In this survey, we have studied the working of various algorithms and methods used for object detection. For this work, we did make a collection of various methods and after that we applied the content-based approach of to recommend the products. Key Words: Object Tracking, Computer vision, Image Retrieval, Recommendation, E-commerce etc. 1. INTRODUCTION In today’s world, most of the people are using social media and watch different videos. If anyone like any object or product in video we can directly provide link through recommendation. As the processing data is huge so we can use cloud based system [7]. In case of video data the background is constantly changing and it is challenging task to detect the object [6]. To deal with this we used novel based approach [6, 8]. To solve the problem of silent object detection in video, we used the concept of virtual border and guided filter and embedding topological features into a deep neural network for extracting semantics [3, 4]. Sometime the data is very complex or crowded and for detecting those complex objects we can use video interlacing to improve multi-object tracking [9]. In many cases it is possible that high amount of time is required for detecting objects. To deal with this issue we use YOLO (You Look Only Once) algorithm [1]. 2. LITERATURE SURVEY AND RELATED WORK Based on the YOLO network author propose a real-time object detection algorithm for videos. Here, author train the fast YOLO model by eliminating the influence of the image background by image pre-processing [1].It is challenging to detect salient objects. In [2] author paper describes about a high-speed video salient object detection method at 0.5s each frame. In [2, 8] author make use of two models, the initial spatiotemporal saliency module and filter based salient temporal propagation module. We can also use [3] embedding topological features into deep neural network for extracting semantics which author use for a salient object detection. Segmentation of input image and compute weight for each region with low level features. Here, the weighted segmentation result is called a topological map and it provide additional channel for CNN. By making use of virtual border and [4] guided filter author trying to propose a novel method for salient object detection in videos. Classification plays an important role in improving object detection. Author used a novel multi-task framework [5] for object detection. A novel multi-task framework uses multi- label classification as an auxiliary task which will improve object detection and can be trained and tested end-to-end. In some cases there may be moving cameras which results into variable background. In [6] author is using novel approach for detecting and tracking objects in videos which are captured by cameras. It is a challenging task to separate actual moving object from the background as both background and foreground changes in each frame of the image sequence. In object detection we need to handle data which may be huge or small. In [7] author make use of automated video analysis system to process large number of video streams. On can get access to huge amount of data using cloud based system. Cloud provide us unlimited storage which results into saving of hardware cost. Some video data frames consist of complex and multiple objects which is sometimes difficult to track. In [9] author is using Multi-Object Tracking-by Detection which is based on a spatio-temporal interlaced encoding video model and specialized DCNN. International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 12 | Dec 2019 www.irjet.net p-ISSN: 2395-0072