{"type":"ScholarlyArticle","title":"Artificial Intelligence-Driven Video Indexing for Rapid Surveillance Footage Summarization and Review","authors":[{"name":"Jaemin Jung","slug":"jaemin-jung","given":"Jaemin","family":"Jung","affiliation":"School of Computer Science and Electrical Engineering , Handong Global University"},{"name":"Soonyong Park","slug":"soonyong-park","given":"Soonyong","family":"Park","affiliation":"School of Computer Science and Electrical Engineering , Handong Global University"},{"name":"Harim Kim","slug":"harim-kim","given":"Harim","family":"Kim","affiliation":"School of Computer Science and Electrical Engineering , Handong Global University"},{"name":"Changha Lee","slug":"changha-lee","given":"Changha","family":"Lee","affiliation":"GMDSOFT"},{"name":"Charmgil Hong","slug":"charmgil-hong","given":"Charmgil","family":"Hong","affiliation":"School of Computer Science and Electrical Engineering , Handong Global University"}],"abstract":"This paper introduces VIDEX, an advanced tool designed to streamline the analysis of surveillance video through a user-friendly interface. VIDEX achieves high development efficiency and maintainability utilizing the Model-View-ViewModel (MVVM) design pattern. The core feature of VIDEX is a footage summary using object detection and anomaly detection. Its architecture ensures efficient data management by organizing detected objects and anomalies within an indexed database, thus facilitating a more rapid review process. Additionally, multi-threading was used to shorten the processing time. VIDEX provides a video summarization that can be used primarily in the criminal investigation stage using the information stored in a database. Discover more about VIDEX and access its resources at https://github.com/nth221/videx.","doi":"10.24963/ijcai.2024/1009","url":"https://ijcai.org/proceedings/2024/1009","pdf":"https://ijcai.org/proceedings/2024/1009.pdf","markdown":"https://ijcai.org/proceedings/2024/1009.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/1009","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Demo Track","track":"Demo Track","number":1009,"pages":{"first":8687,"last":8690},"keywords":["Data Mining: DM: Anomaly/outlier detection","Computer Vision: CV: Recognition (object detection, categorization)","Machine Learning: ML: Applications"],"videos":[],"references":[{"position":1,"raw":"[Aggarwal, 2017] Charu C. Aggarwal. Outlier analysis. Springer, 2017.","title":"Outlier analysis","authors":"C Charu, Aggarwal","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bai et al., 2019] Junjie Bai, Fang Lu, Ke Zhang, et al. Onnx: Open neural network exchange. https://github.com/ onnx/onnx, 2019.","title":"Onnx: Open neural network exchange","authors":"Junjie Bai, Fang Lu, Ke Zhang","year":null,"venue":null,"doi":null,"url":"https://github.com/onnx/onnx,2019","internal_url":null},{"position":3,"raw":"[Breunig et al., 2000] Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, and Jörg Sander. Lof: iden- tifying density-based local outliers. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data, pages 93-104, 2000.","title":"Lof: identifying density-based local outliers","authors":"Markus M Breunig, Hans-Peter Kriegel, Raymond T Ng, Jörg Sander","year":"2000","venue":"Proceedings of the 2000 ACM SIGMOD international conference on Management of data","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Carreira and Zisserman, 2017] Joao Carreira and Andrew Zisserman. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 6299-6308, 2017.","title":"Quo vadis, action recognition? a new model and the kinetics dataset","authors":"Joao Carreira, Andrew Zisserman","year":"2017","venue":"proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[He et al., 2003] Zengyou He, Xiaofei Xu, and Shengchun Deng. Discovering cluster-based local outliers. Pattern recognition letters, 24(9-10):1641-1650, 2003.","title":"Shengchun Deng. Discovering cluster-based local outliers","authors":"He","year":"2003","venue":"Pattern recognition letters","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Jocher, 2020] Glenn Jocher. Ultralytics yolov5, 2020.","title":"Ultralytics yolov","authors":"Glenn Jocher","year":"2020","venue":"Ultralytics yolov","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kim et al., 2023] Harim Kim, Chang Ha Lee, and Charmgil Hong. Crime scene detection in surveillance videos using variational autoencoder-based support vector data descrip- tion. In Asian Conference on Intelligent Information and Database Systems, pages 451-464. Springer, 2023.","title":"Crime scene detection in surveillance videos using variational autoencoder-based support vector data description","authors":"Kim","year":"2023","venue":"Asian Conference on Intelligent Information and Database Systems","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Redmon et al., 2016] Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 779-788, 2016.","title":"You only look once: Unified, real-time object detection","authors":"Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi","year":"2016","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Zhao et al., 2018] Xun Zhao, Yanhong Wu, Dik Lun Lee, and Weiwei Cui. iforest: Interpreting random forests via visual analytics. IEEE transactions on visualization and computer graphics, 25(1):407-416, 2018.","title":"iforest: Interpreting random forests via visual analytics","authors":"Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui","year":"2018","venue":"IEEE transactions on visualization and computer graphics","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}