Reproducibility of AOD Algorithm: An Experimental evaluation for Key-Predictors Identification

• Main Authors: Monika Monika, Kamaldeep Kaur
• Format: Article
• Language: English
• Published: European Alliance for Innovation (EAI) 2020-05-01
• Series: EAI Endorsed Transactions on Context-aware Systems and Applications
• Subjects: abandoned object detection; aod algorithm; benchmark dataset; reproducibility; video processing
• Online Access: https://eudl.eu/pdf/10.4108/eai.13-7-2018.164099

INTRODUCTION: Today surveillance systems are widespread across the globe for monitoring of various activities.Abandoned Object Detection (AOD) and identifying its location is one of them. In this paper, we evaluated thereproducibility of an existing AOD algorithm on benchmark video datasets.OBJECTIVES: The purpose of the study is to identify the key predictors for developing a generalized AOD algorithm.METHODS: The algorithm selection is performed by a detailed exploration of repositories through various researchquestions (RQs).RESULTS: After the study video summarization, Correct Detection Rate (CDR), generalized Region of Interest (ROI),background learning, and interaction factor considered for enhancing the AOD algorithm.CONCLUSION: Identification of suspiciousness has various measures depending upon perception, on the basis of resultsexplored the existing algorithm can be improved using key-predictors with observational parameters.