VIDEO CLONE DETECTOR USING HADOOP

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1 VIDEO CLONE DETECTOR USING HADOOP Abstract Ms. Nikita Bhoir 1 Ms. Akshata Kolekar 2 Internet has become the most important part in the people s day-to-day life. It is widely used due to abundant resources. This available data are expressed in many forms such as text, pictures, audio, video and so on. Due to interest in videos there are various sites which provides with different kinds of videos, but it is not necessary that every video hold original content. The purpose of video copy detection is to differentiate between original and duplicate videos and find out the similarities between the content of the two videos. This is done with the help of two video copy detection techniques i.e. Frame Fusion based on Path Merging and Query Content Prediction. Due to billions of video present over the internet, it is not possible to perform the video copy detection process on a single machine approach as it is a hectic process. Using distributed computing platform i.e. Hadoop, efficiency would be improved. It is applied to calculate the hash values of a large number of videos and then match them. Keywords Video Copy Detection, Video Hash, Hadoop, apreduce, Path merging, Frame Fusion, Content Query Prediction. Introduction Nowadays internet is commonly used by the people as huge number of data is available on the internet. This data is in the form of text, audio, video, etc. Out of this, video is the one which is most trending among people because of its clear expression as video depicts the concept more clearly compared to that of audio, text which consumes a lot of time. But the problem is that the huge numbers of videos already exist on the internet and also more and more videos are generated every day. Due to which more storage space is required to store this videos. Among these large number of videos, most of the videos are duplicated copies of original ones. When the users browse for the video of his/her interest then he/she has to go through this number of near 1 Department of Information TechnologyBharati Vidyapeeth College of EngineeringNavi Mumbai, India Id nikitabhoir.bhoir2@gmail.com 2 Department of Information TechnologyBharati Vidyapeeth College of EngineeringNavi Mumbai, India Id akshata.kolekar27@gmail.com

2 duplicated copies. Because of all these users time is wasted. To avoid this scenario, the need of video copy detection is emerging rapidly. Video copy detection is to detect the copied video from the original ones by comparing the content within them. The contents can be calculated by using their hash values. It is difficult to achieve video copy detection using a single machine approach. This problem can be solved using Hadoop which is a distributed platform. In this MapReduce programming model is used, which uses HDFS (Hadoop Distributed File System) as it is a distributed file storage system. It is also use to achieve more reliable file storage. In order to calculate and match the hash values of these large numbers of videos Hadoop is used. Two video copy detection algorithms which are Frame Fusion based on Path Merging and Query Content Prediction is implemented on the Hadoop platform to detect the copied videos. One of the major technique in the existing system is Watermarking Technique. In this technique the invisible signal is embedded into video. Then the video is converted into frames which have watermark on it. The watermark in the video is used to detect whether the video is copied or not.[3] The limitation of watermarking technique is that watermark should be present on the original video. If the watermark is not present on the original video it is difficult to achieve the video copy detection. Introduction to Hadoop Hadoop is a distributed platform makes use of MapReduce Programming model. It has two functions i.e., Map function and Reduce function. The unstructured data is stored in the form of Key-Value pairs in HDFS using these functions. In Mapping function, conversion of frames into key value pair takes place. The intermediate key-value pair is then sent to the Reduce function. In Reduce function, key-value pair is converted into hash value and they are stored in the HDFS.[2]

3 Proposed System The proposed system is used to detect the copied videos and find out the similarities between the given two videos. The system uses Frame Fusion based on Path Merging and Query Content Prediction techniques to detect the copied videos. The system will use the distributed computing platform i.e., Hadoop which will significantly improve the efficiency of the video copy detection.[1] System Architecture Figure 2: System Architecture It contains HDFS for storing the training video. The user would upload a query video clip. After that the video matching process takes place, in that we would be using two technique Path Merging and Query Content Prediction. By using this technique we will perform copy detection. The first method is the Path Merging based on Frame Fusion. In this method, the video is converted into the frames and the indexing of the frame is done. Then from the indexed frames which are considered as nodes, a graph is created and the path for the same is calculated. If the

4 path of both the videos are matched then the video is considered as the copied video or else the next method that is query content prediction comes into picture. In this technique the video is converted into the audio and then the audio is converted into the text and if the text of both the video matches then the video is considered as a copied video. The text can be in the form of subtitles or any other text in the videos. Figure 3: Flowchart The user logs in to the system with user name and password. If the user is the valid user then he/she gets the access to the system.

5 Implementation In Implementation phase, similarities between the videos are found out using the Hadoop platform by making use of two phases, i.e. Map and Reduce. The two distinct techniques which are being used are Frame Fusion based on Path Merging and Query Content Prediction. The main aim of these techniques is to convert the videos into multiple frames, extracting their features and then finally comparing the both querying as well as the training videos. The training video is already present in the HBase, while the querying video is taken as an input from the user.

6 In figure 4, consider that four frames are drawn from the video. IF FRAME A is equal to FRAME a, FRAME B is equal to FRAME b, FRAME C is equal to FRAME c, FRAME D is equal to FRAME d, Then the querying video is said to be 100% copied. IF FRAME A is not equal to FRAME a, FRAME B is equal to FRAME b, FRAME C is equal to FRAME c, FRAME D is equal to FRAME d, Then the querying video is said to be 75% copied. i.e. if any of the one frame out of four matches with the training video, then it is said to be 75% copied. IF FRAME A is not equal to FRAME a, FRAME B is not equal to FRAME b, FRAME C is equal to FRAME c, FRAME D is equal to FRAME d, Then the querying videos is said to be 50% copied. i.e. if any two of the frames out of four matches with the training videos, then it is said to be 50% copied. IF FRAME A is not equal to FRAME a, FRAME B is not equal to FRAME b, FRAME C is not equal to FRAME c, FRAME D is equal to FRAME d, Then the querying videos is said to be 25% copied. i.e. if any of the three of the frames out of four matches with the training videos, then it is said to be 25% copied.

7 IF FRAME A is not equal to FRAME a, FRAME B is not equal to FRAME b, FRAME C is not equal to FRAME c, FRAME D is not equal to FRAME d, Then the querying video is said to be 0% copied, hence it isn t a copied video. Conclusion In this paper, video copy detection is done which is differentiated from the existing ones for the path merging algorithm using frame fusion and query content prediction. The proposed one is able to not only achieve the copy detection between the videos but also find the similarities between the two given videos. The efficiency of the Hadoop platform is also focused which has high processing speed and also makes use of HBase which is optimum for storing and retrieval of data. References 1. Nan Nan and Guizhong Liu, Video Copy Detection based on Path Merging and Query Content Prediction, IEEE Transactions on Circuit and Systems for Video Technology, VOL. 25, NO. 10, October Jing Li, Xuquan Lian, Qiang Wu, Jiande Sun, Real Time Video Copy Detection based on Hadoop, Sixth International Conference on Information Science andtechnologydalian, China; May 6-8, Upasana Yadav, J. P. Sharma, Dinesh Sharma, Purnima K Sharma, Different Watermarking Techniques & its Applications: A Review, International journal of scientific and engineering research, Volume 5, Issue 4, April