Moving shadow detection based on stationary wavelet transform

Nagarathinam, Kavitha; Kathavarayan, Ruba Soundar

doi:10.1186/s13640-017-0198-x

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…The Stationary Wavelet Transform (SWT) algorithm is used for the fusion process as demonstrated in [69,70]. SWT decomposes the input signals into scaling and Wavelet coefficients, enabling the preservation of the image texture and edge information while reconstructing the fused signals from the sub-bands back to the image.…”

Section: Processing Image Fusion Methodsmentioning

confidence: 99%

Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

Ben-Shoushan

Brook

2023

Remote Sensing

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Smart vehicles with embedded Autonomous Vehicle (AV) technologies are currently equipped with different types of mounted sensors, aiming to ensure safe movement for both passengers and other road users. The sensors’ ability to capture and gather data to be synchronically interpreted by neural networks for a clear understanding of the surroundings is influenced by lighting conditions, such as natural lighting levels, artificial lighting effects, time of day, and various weather conditions, such as rain, fog, haze, and extreme temperatures. Such changing environmental conditions are also known as complex environments. In addition, the appearance of other road users is varied and relative to the vehicle’s perspective; thus, the identification of features in a complex background is still a challenge. This paper presents a pre-processing method using multi-sensorial RGB and thermal camera data. The aim is to handle issues arising from the combined inputs of multiple sensors, such as data registration and value unification. Foreground refinement, followed by a novel statistical anomaly-based feature extraction prior to image fusion, is presented. The results met the AV challenges in CNN’s classification. The reduction of the collected data and its variation level was achieved. The unified physical value contributed to the robustness of input data, providing a better perception of the surroundings under varied environmental conditions in mixed datasets for day and night images. The method presented uses fused images, robustly enriched with texture and feature depth and reduced dependency on lighting or environmental conditions, as an input for a CNN. The CNN was capable of extracting and classifying dynamic objects as vehicles and pedestrians from the complex background in both daylight and nightlight images.

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Section: Processing Image Fusion Methodsmentioning

confidence: 99%

Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

Ben-Shoushan

Brook

2023

Remote Sensing

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“…( 3), are applied on the images obtained from the CMOS camera to remove the shadows. The infrared segmented images do not contain shadows, thus, fusion operation is performed on both types of images to remove shadows [15]. Fused HL (i, j) = C HL (i, j) ∩ T HL (i, j) Fused HH (i, j) = C HH (i, j) ∩ T HH (i, j)…”

Section: Wavelet-based Fusion For Shadow Removalmentioning

confidence: 99%

Reconfigurable Pedestrian Detection System Using Deep Learning for Video Surveillance

JEEVARAJAN,

NIRMAL KUMAR

2023

IEICE Trans. Inf. & Syst.

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We present a reconfigurable deep learning pedestrian detection system for surveillance systems that detect people with shadows in different lighting and heavily occluded conditions. This work proposes a region-based CNN, combined with CMOS and thermal cameras to obtain human features even under poor lighting conditions. The main advantage of a reconfigurable system with respect to processor-based systems is its high performance and parallelism when processing large amount of data such as video frames. We discuss the details of hardware implementation in the proposed real-time pedestrian detection algorithm on a Zynq FPGA. Simulation results show that the proposed integrated approach of R-CNN architecture with cameras provides better performance in terms of accuracy, precision, and F1-score. The performance of Zynq FPGA was compared to other works, which showed that the proposed architecture is a good tradeoff in terms of quality, accuracy, speed, and resource utilization.

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“…SWT has been previously used in other studies, e.g. [16]. To the best of our knowledge, the proposed approach is the first to combine EWT multiresolution analysis with deep learning for both short-and long-term stock market forecasting.…”

Section: Introductionmentioning

confidence: 99%

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

2021

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Due to its complexity, financial time-series forecasting is regarded as one of the most challenging problems. During the past two decades, nonlinear modeling techniques, such as artificial neural networks, were commonly employed to solve a variety of time-series problems. Recently, however, deep neural network has been found to be more efficient than those in many application domains. In this article, we propose a model based on deep neural networks that improves the forecasting of stock prices. We investigate the impact of combining deep learning techniques with multiresolution analysis to improve the forecasting accuracy. Our proposed model is based on an empirical wavelet transform which is shown to outperform traditional stationary wavelet transform in capturing price fluctuations at different time scales. The proposed model is demonstrated to be substantially more effective than other models when evaluated on the S&P500 stock index and Mackey-Glass time series.

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Moving shadow detection based on stationary wavelet transform

Cited by 9 publications

References 29 publications

Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

Fused Thermal and RGB Imagery for Robust Detection and Classification of Dynamic Objects in Mixed Datasets via Pre-Trained High-Level CNN

Reconfigurable Pedestrian Detection System Using Deep Learning for Video Surveillance

Combining Deep Learning and Multiresolution Analysis for Stock Market Forecasting

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