Clustering of Human Beings in an Image and Comparing the Techniques on the basis of Accuracy and Time (original) (raw)
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International Journal of Engineering Development and Research, 2015
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Object detection is one of the most important areas in the fields of Data Science and Computer Vision. In this paper, we present a novel approach to identifying and tracking groups of people, couples, and individuals in videos by using deep learning-based object detection and object tracking techniques along with a proposed grouping algorithm. In this approach, transfer learning is applied on YOLO v3 model for the detection of people in video frames, and Deep SORT is applied for tracking each detected person throughout the video. Results obtained from person detection and person tracking were used by the proposed grouping algorithm to identify and track groups, couples, and individuals who are appearing in input videos. Our proposed grouping algorithm is based on the proximity between each individual and the time duration that proximity is maintained for. It also considers how to identify and track groups, when people are moving within the groups. This approach was evaluated using C...
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With a rapid speed of technology human detection in an image or in video plays a vital role in many computer vision fields. It has various applications with respect to sensitivity, security and secrecy. Because of various imaging conditions like similarity to other people, size, environmental conditions, resolution, poses and rotation human detection go through different challenges. There are many methods for detecting humans but skin color modelling, background subtraction, K-Means clustering is used here because of its effectiveness and it can be easily separated from the other objects present in the image and background.
IJERT-A Survey on Data Mining Techniques for Image Grouping
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/a-survey-on-data-mining-techniques-for-image-grouping https://www.ijert.org/research/a-survey-on-data-mining-techniques-for-image-grouping-IJERTV2IS90732.pdf Production of digital images from cameras, mobile phones, camcorders, video films and scanned images has increased exponentially in the past decade. Increasing number of digital devices have influenced human to an extent of taking the same scenes in multiple views. Commercial Organizations, Agencies and Educational Institutions conduct events round the year and the image acquisition activity through the digital devices installed at the event venues is predominant in Universities and Colleges. It is evident that the cost of recording the events is negligible compared to the storage requirement over the years for future reference and inference. Thus the captured images must be preprocessed to avoid duplication and parsed for low-level processing such as noise removal, contrast enhancement and image sharpening. Few of the processed images can then be chosen based on the weights associated with the event sessions using mid-level processing techniques such as object detection and recognition. Further, the chosen images can be indexed, captioned to be maintained as an archive and stored in the databases to extend quick summary of events for preparing annual documents, editing magazines and departmental newsletters. This paper explores different data mining techniques namely classification and clustering to automatically group the images.
A Survey on Data Mining Techniques for Image Grouping
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Production of digital images from cameras, mobile phones, camcorders, video films and scanned images has increased exponentially in the past decade. Increasing number of digital devices have influenced human to an extent of taking the same scenes in multiple views. Commercial Organizations, Agencies and Educational Institutions conduct events round the year and the image acquisition activity through the digital devices installed at the event venues is predominant in Universities and Colleges. It is evident that the cost of recording the events is negligible compared to the storage requirement over the years for future reference and inference. Thus the captured images must be preprocessed to avoid duplication and parsed for low-level processing such as noise removal, contrast enhancement and image sharpening. Few of the processed images can then be chosen based on the weights associated with the event sessions using midlevel processing techniques such as object detection and recognition. Further, the chosen images can be indexed, captioned to be maintained as an archive and stored in the databases to extend quick summary of events for preparing annual documents, editing magazines and departmental newsletters. This paper explores different data mining techniques namely classification and clustering to automatically group the images.
Clustering Techniques Implementation for Detecting Faces in Digital Image
pasca.if.its.ac.id
Research in face detection has attracted many researchers due to it is a crucial preprocessing step for many advance applications such as face recognition, video conferencing, security surveillance and entertainment-related applications. The rapid development of computer and ...
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Image and video (multimedia) databases are very large in size. It becomes very difficult to search the images from these databases by the application of conventional exhaustive searching because it will require unreasonable amount of time. For this purpose an automatic graph based clustering algorithm is developed and proposed through this paper. It reduces the searching time for the images from large databases. The proposed algorithm works on the concept of minimum spanning tree, which removes the inconsistent edges from tree, based on the dynamic threshold provided to the algorithm. The proposed algorithm reduces the search time for the retrieval with an acceptable loss in the accuracy.
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