International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8 Issue-3, September 2019 7895 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: C6564098319/2019©BEIESP DOI:10.35940/ijrte.C6564.098319 Abstract: Video surveillance data in smart cities needs to analyze a large amount of video footage in order to locate the people who are violating the traffic rules. The fact is that it is very easy for the human being to recognize different objects in images and videos. For a computer program this is quite a difficult task. Hence there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos. One major application of trajectory object detection is the Intelligent Transport Systems (ITS). Vehicle type detection, tracking and classification play an important role in ITS. In order to analyze huge amount of video footage deep learning algorithms have been deployed. The main phase of vehicle type detection includes annotating the data, training the model and validating the model. The problems and challenges in identifying or detecting type of vehicle are due to weather, shadows, blurring effect, light condition and quality of the data. In this paper deep learning algorithms such as Faster R CNN and Mask R CNN and Frameworks like YOLO were used for the object detection. Dataset (different types of vehicle pictures in video format) were collected both from in-house premises as well as from the Internet to detect and recognize the type of vehicles which are common in traffic systems. The experimental results show that among the three approaches used the Mask R CNN algorithm is found to be more efficient and accurate in vehicle type detection. Keywords : Deep learning, Intelligent Transport Systems, Mask RCNN, YOLO I. INTRODUCTION Image processing is the process of analyzing and manipulating an image by using a computer algorithms in-order to extract useful information or to enhance the quality of the image. This can be used in the process of finding instances of the real world objects such as human, animals, birds, faces, bicycles, car, truck and building etc. Video content analysis (VCA) paves way to analyze and detect the various temporal and spatial details available in the video. Instead of analysis the still image, video contents can be analyzed in-order to extract useful insight from it. Insights from VCA can be used in various domains such as health care, home automation and automotive industry in-order to provide safety and security. Trajectory Object detection and classification is a part of VCA. There is an urgent need for intelligent transport systems to replace human operation to monitor the surveillance area and to analyze a large amount of video footage in order to locate the people who are violating Revised Manuscript Received on September 19, 2019. S Anitha Elavarasi*, Department of Computer Science & Engineering, Sona College of Technology, Salem, India. Email: anithaelavarasi@sonatech.ac.in J Jayanthi, Department of Computer Science & Engineering, Sona College of Technology, Salem, India . Email: jayanthij@sonatech.ac.in N Basker, Department of Computer Science & Engineering, Sona College of Technology, Salem, India . Email: baskern@sonatech.ac.in the traffic rules. Huge volume of video footage has to be analyzed in traffic control system especially in smart city application. There is a need for deep learning algorithm to easy the task. Deep learning is a part of machine learning methods based on artificial neural networks. Deep Learning algorithm such as Convolutional Neural Network (CNN)[9], Faster R-CNN [6] has been widely used in the field of object detection, segmentation and classification. The CNN approach consists of neurons, activation function and an output. Neuron contains learning units. The neurons in CNN receive several inputs, take a weighted sum over them, pass it through an activation function and finally respond with an output. The most common challenges associated with detection of vehicles are due to weather condition, shadows, blurring effect, light condition, type of vehicle and quality of the input data etc. In this paper section 2 describes the related work and section 3 explains the architecture of Trajectory Object detection and classification system. Section 4 describes the results and discussion and finally section 5 concludes the work. II. RELATED WORK Yong Tang et al present vehicle detection and recognition. The author uses automatic monitoring digital cameras to take snapshots of moving motion pictures [1]. From the collected images, sequences are extracted to represent features of a vehicle. Initially machine learning algorithms like Haar-like feature and Adaboost algorithm are applied for feature extracting and constructing classifiers which is used to locate the vehicle over the input image. The drawback of the system is that it only works for day light image. Viktoria Plemakova et al describe vehicle detection based on convolutional neural networks [3]. The main aim of the author is to train, classify and detect the vehicles from different angles using neural networks. The proposed CNN contains 6 convolutional layers and 5 max pooling layers. The challenges faced by the author are due to weather, light conditions and vehicle type diversity. Yilmaz describes the vehicle detection using deep learning methods. The method takes five stages such as (1) loading the data, (2) the design of the Convolutional Neural Network, (3) training and configuration, (4) training of the R-CNN approach and (5) evaluation of the detector [4]. The parameters to be considered are image windows, selective search for object recognition, category independent object proposals, object segmentation using constrained parametric min-cuts and multiscale combinatorial grouping. Trajectory Object Detection using Deep Learning Algorithms S Anitha Elavarasi, J Jayanthi, N Basker