Detection of Anomalous Gait as Forensic Gait in Residential Units Using Pre-trained Convolution Neural Networks
Published on Mar 5, 2020
· DOI :10.1007/978-3-030-39442-4_57
One of the advantages of transfer learning technique is its capability to learn new dataset using its finest pre-trained architecture. Other advantages of this technique are small dataset requirements along with faster learning process that could yield high accuracy results. Hence in this paper, anomalous gait detection or also known as forensic gait during housebreaking crime at the gate of residential units is discussed with transfer learning technique based on five popular pre-trained convolution neural networks (CNNs) as classifiers. High accuracy and sensitivity are achieved from remodeled of the pre-trained CNNs for the learning process, offline test, and real-time test. The accuracy attained from remodeled of the pre-trained CNNs have pledged high potential towards developing the forensic intelligent surveillance technique.