Cluster Comput DOI 10.1007/s10586-017-1244-2 Novel effective X-path particle swarm optimization based deprived video data retrieval for smart city S. Thanga Ramya 1 · Bhuvaneshwari Arunagiri 2 · P. Rangarajan 3 Received: 4 April 2017 / Revised: 15 September 2017 / Accepted: 5 October 2017 © Springer Science+Business Media, LLC 2017 Abstract With the tremendous increase in low resolution videos on video sharing websites, retrieval of a correct video becomes a tougher task. The existing methods provide retrieval approaches based on minimum number of features comparison. It leads to an inefficient video retrieval. Most researches had concentrated on tracking ability and con- version of low resolution to high resolution videos. These methods failed to provide fast retrieval of videos from large databases. The proposed work is concentrated mostly on riot videos from large video repositories to identify the previous criminal records in a particular region of the smart city (Coc- chia in Smart and digital city: a systematic literature review, Springer International Publishing, Switzerland, 2014; Pardo and Taewoo in Proceedings of the 12th annual international conference on digital government research, ACM, New York, 2011). It uses certain combination ofobject oriented features like object and camera motion feature, color histogram and edge detection technique. In the proposed retrieval process, the key frames are extracted from the original video instead of using the whole video information for retrieval process. Object Oriented features were then extracted from these key frames and saved in database. Then, the retrieval process is B Bhuvaneshwari Arunagiri bhuvan@adhiparasakthi.in S. Thanga Ramya str.it@rmd.ac.in P. Rangarajan rangarajan69@gmail.com 1 Department of Information Technology, RMD Engineering College, Chennai, India 2 Department of Information Technology, Adhiparasakthi Engineering College, Melmaruvathur, India 3 Department of Computer Science and Engineering, RMD Engineering College, Chennai, India done by searching the availability of relevant Object Ori- ented values based on the query submitted by the user. Thus the combination of four different features provides an effi- cient retrieval of low resolution videos from the database. The retrieved video may include redundant information in the projected work. To avoid such redundancy, particle swarm optimization (PSO) is used. The result of query video is compared with database video using degree of closeness measurement. Consequently, low resolution video retrieval based on PSO seems to be encouraging in terms of its perfor- mance in extracting videos than existing retrieval approaches. Keywords Video retrieval · Particle swarm optimization · Videos · Database · Video attributes · Smartcity 1 Introduction Low resolution videos has huge amount of varied informa- tion at different frames like color, pixels, shots and scenes. The low resolution videos are increased in quantity. Due to high level compression of the videos, processing the entire information for video retrieval systems requires more compu- tational time and provides inaccurate results. Eliminating the redundant information [3] from these low resolution videos improves the retrieval time and speed. Video summarization is the commonly used method to set up an excellent video archiving system. In which, the video summaries are con- sidered to be stationary or dynamic features of the images [4]. Widely used video summarization method is key frame extraction that renders meaningful and limited information from the video contents [5]. Extraction of features from key frames in the form of Object Oriented format creates a new approach to summarize videos. Most importantly, arrang- ing the extracted Object Oriented features chronologically is 123