Feature based video stabilization based on boosted HAAR Cascade and representative point matching algorithm

Raj, Rohit; Rajiv, Pooshkar; Kumar, Parvinder; Khari, Manju; Verdú, Elena; Crespo, Rubén González; Manogaran, Gunasekaran

doi:10.1016/j.imavis.2020.103957

Cited by 18 publications

(9 citation statements)

References 15 publications

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“…However, the Haar features strongly depend on the orientation of the object being monitored, especially on the angle of rotation [7][8][9]. Histogram of Oriented Gradients (HOG) has similar problems.…”

Section: Motivationmentioning

confidence: 99%

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

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Automation, including machine learning technologies, are becoming increasingly crucial in agriculture to increase productivity. Machine vision is one of the most popular parts of machine learning and has been widely used where advanced automation and control have been required. The trend has shifted from classical image processing and machine learning techniques to modern artificial intelligence (AI) and deep learning (DL) methods. Based on large training datasets and pre-trained models, DL-based methods have proven to be more accurate than previous traditional techniques. Machine vision has wide applications in agriculture, including the detection of weeds and pests in crops. Variation in lighting conditions, failures to transfer learning, and object occlusion constitute key challenges in this domain. Recently, DL has gained much attention due to its advantages in object detection, classification, and feature extraction. DL algorithms can automatically extract information from large amounts of data used to model complex problems and is, therefore, suitable for detecting and classifying weeds and crops. We present a systematic review of AI-based systems to detect weeds, emphasizing recent trends in DL. Various DL methods are discussed to clarify their overall potential, usefulness, and performance. This study indicates that several limitations obstruct the widespread adoption of AI/DL in commercial applications. Recommendations for overcoming these challenges are summarized.

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Section: Motivationmentioning

confidence: 99%

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

2021

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“…The process of calculating global motion vector is motion estimation. Motion estimation methods mainly include block matching method, gray projection method, phase correlation matching method, bit plane matching method, gray projection method, feature matching method, optical flow method and so on [5]. Block matching method and feature matching method are considered to have higher matching accuracy.…”

Section: Introductionmentioning

confidence: 99%

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

Zhang

Yang

et al. 2022

Chin. J. Mech. Eng.

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Noise, vibration and harshness (NVH) problems in vehicle engineering are always challenging in both traditional vehicles and intelligent vehicles. Although high accuracy manufacturing, modern structural roads and advanced suspension technology have already significantly reduced NVH problems and their impacts; off-road condition, obstacles and extreme operating condition could still trigger NVH problems unexpectedly. This paper proposes a vehicular electronic image stabilization (EIS) system to solve the vibration problem of the camera and ensure the environment perceptive function of vehicles. Firstly, feature point detection and matching based on an oriented FAST and rotated BRIEF (ORB) algorithm are implemented to match images in the process of EIS. Furthermore, a novel improved random sampling consensus algorithm (i-RANSAC) is proposed to eliminate mismatched feature points and increase the matching accuracy significantly. And an adaptive Kalman filter (AKF) is applied to improve the adaptability of the vehicular EIS. Finally, an experimental platform based on a gasoline model car was established to validate its performance. The experimental results show that the proposed EIS system can satisfy vehicular performance requirements even under off-road condition with obvious obstacles.

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“…The purpose of video stabilization is to suppress or weaken the impact of camera jitter on video quality and to satisfy people's perception and subsequent processing in the future. The video stabilization method can be mainly divided into three categories, i.e., mechanical image stabilization technology, optical image stabilization technology, and electronic image stabilization technology [4][5][6]. Mechanical image stabilization detects the jitter of the camera platform through gyro sensors and other devices and then adjusts the servo system to stabilize the image [7].…”

Section: Introductionmentioning

confidence: 99%

Self-Calibration Spherical Video Stabilization Based on Gyroscope

Fang

Chen

2021

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With the development of handheld video capturing devices, video stabilization becomes increasingly important. The gyroscope-based video stabilization methods perform promising ability, since they can return more reliable three-dimensional (3D) camera rotation estimation, especially when there are many moving objects in scenes or there are serious motion blur or illumination changes. However, the gyroscope-based methods depend on the camera intrinsic parameters to execute video stabilization. Therefore, a self-calibrated spherical video stabilization method was proposed. It builds a virtual sphere, of which the spherical radius is calibrated automatically, and then projects each frame of the video to the sphere. Through the inverse rotation of the spherical image according to the rotation jitter component, the dependence on the camera intrinsic parameters is relaxed. The experimental results showed that the proposed method does not need to calibrate the camera and it can suppress the camera jitter by binding the gyroscope on the camera. Moreover, compared with other state-of-the-art methods, the proposed method can improve the peak signal-to-noise ratio, the structural similarity metric, the cropping ratio, the distortion score, and the stability score.

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Feature based video stabilization based on boosted HAAR Cascade and representative point matching algorithm

Cited by 18 publications

References 15 publications

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

Deep Neural Networks to Detect Weeds from Crops in Agricultural Environments in Real-Time: A Review

Vehicular Electronic Image Stabilization System Based on a Gasoline Model Car Platform

Self-Calibration Spherical Video Stabilization Based on Gyroscope

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