Anisotropic Sophisticated Spatio-Temporal Contours Based Deep Neural Learned Moving Objects Detection in Video
K.N. Abdul Kader1, Nihal2
1K.N. Abdul Kader, Assistant Professor, PG and Research Department of Computer Science, Jamal Mohamed College (Autonomous), [Affiliated to Bharathidasan University], Tiruchirappalli, TN, India.
2Nihal, Assistant Professor, PG and Research Department of Computer Science, Jamal Mohamed College (Autonomous), [Affiliated to Bharathidasan University], Tiruchirappalli, TN, India.
Manuscript received on November 20, 2019. | Revised Manuscript received on November 28, 2019. | Manuscript published on 30 November, 2019. | PP: 7293-7300 | Volume-8 Issue-4, November 2019. | Retrieval Number: D5288118419/2019©BEIESP | DOI: 10.35940/ijrte.D5288.118419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Object detection in the video sequence is a significant problem to be resolved in image processing because it used different applications in video compression, video surveillance, robot technology, etc. Few research works have been designed in conventional works to discover moving objects using various machine learning techniques. However, dynamic changing background, object size variations and degradation of video frames during the object detection process remained an open issue. In order to overcome such limitations, Anisotropic Sophisticated Spatiotemporal Contours based Deep Neural Network Learning (ASSC-DNNL) practice is projected. ASSC-DNL Technique initially obtains a number of video file as input at the input layer. After acquiring the video, input layer forward it to hidden layers. Subsequently, ASSC-DNL Technique accomplishes the encoding process in the first hidden layer using Anisotropic Stacked Autoencoder (ASA). During the encoding process, ASSC-DNL practice maps each video frames pixels in input video via code. This practice results in compressed video with enhanced quality. Afterward, ASSC-DNL practice transforms compressed video into a numeral of frames in the second concealed layer. Followed by, ASSC-DNL practice carried out Teknomo–Fernandez Spatiotemporal Based Background Subtraction (TS-BS) process at the third hidden layer, in which it effectively segments the foreground images from dynamic changing background. Then, ASSC-DNL practice deep analyzes the foreground image of video frames and mines some features like shape, color, texture, intensity, and size. Finally, ASSC-DNL Technique exactly finds the moving objects in video frames according to identified features with minimal time at the output layer. Therefore, ASSC-DNL Technique obtains enhanced moving objects detection performance when compared to existing works. The simulation of ASSC-DNL practice is conducted via different metrics such as accuracy, time and false positive rate towards in detection.
Keywords: Anisotropic Stacked Autoencoder, Contours Based Object Size Estimation, Dynamic Background, Gaussian Activation Function, Video Compression, Video Frames.
Scope of the Article: Foundations Dynamics.