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How to detect objects in images using Opencv?

Feature (computer vision)

Feature extraction

Object (computer science)

Active shape model

Object detection

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Proceedings Article•DOI Developing a framework for fruits detection from images Md. Shaddam Hossen, Mohammad Shamsul Arefin, Rezaul Karim - Show less +2 more 01 Feb 2017 5 Citations	Considering this fact, in this paper, we propose a method using colour, shape and texture for detecting objects from images.
Proceedings Article•DOI 3D Probabilistic Feature Point Model for Object Detection and Recognition Sami Romdhani, Thomas Vetter - Show less +1 more 17 Jun 2007 25 Citations	This paper presents a novel statistical shape model that can be used to detect and localise feature points of a class of objects in images.
Book Chapter•DOI Effective Moving Object Detection from Videos Captured by a Moving Camera Wu-Chih Hu, Chao-Ho Chen, Chih-Min Chen, Tsong-Yi Chen - Show less +3 more 01 Jan 2014 14 Citations	This paper presents an effective method to detect moving objects for videos captured by a moving camera.
Proceedings Article•DOI Real-time moving vehicle detection, tracking, and counting system implemented with OpenCV Da Li, Bodong Liang, Weigang Zhang - Show less +2 more 26 Apr 2014 68 Citations	Experimental results, implemented in Visual C++ code with OpenCV development kits, indicate that the proposed method can detect, track, and count moving vehicles accurately.
Open access•Journal Article•DOI Small Object Detection with Multiscale Features Guoxiong Hu, Guoxiong Hu, Zhong Yang, Lei Hu, Li Huang, Jiaming Han - Show less +5 more 30 Sep 2018-International Journal of Digital Multimedia Broadcasting 50 Citations	In this paper, we can achieve good detection accuracy by extracting the features at different convolution levels of the object and using the multiscale features to detect small objects.
Proceedings Article•DOI Object detection with novel shape representation using bounding edge fragments Mohammad Anvaripour, Hossein Ebrahimnezhad - Show less +1 more 01 Dec 2010 2 Citations	This paper proposes a new method to detect objects in images.
Open access•Proceedings Article•DOI Multiple object detection by sequential monte carlo and Hierarchical Detection Network Michal Sofka, Jingdan Zhang, S. Kevin Zhou, Dorin Comaniciu - Show less +3 more 13 Jun 2010 29 Citations	In this paper, we propose a novel framework for detecting multiple objects in 2D and 3D images.

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Why are object detectors are used for license plate detection?4 answersObject detectors are utilized for license plate detection due to their crucial role in enhancing the accuracy and robustness of Automatic License Plate Recognition (ALPR) systems. These detectors play a vital part in identifying license plates amidst varying conditions such as changes in size, orientation, color, background, and resolution, ensuring the system's efficiency in real-world scenarios. Object detection models, particularly those based on deep learning frameworks like YOLOV3, are employed to detect license plates accurately even in challenging situations like poor quality surveillance videos, occlusion, and fluctuating weather conditions. By training custom models on specific datasets, object detectors enhance the overall performance of ALPR systems, enabling efficient detection and recognition of license plates for various applications, including traffic rule enforcement and vehicle tracking.

How can image processing be used to detect and recognize objects in the scene?4 answersImage processing can be used to detect and recognize objects in the scene by utilizing various techniques and models. One approach is to use deep learning-based convolutional neural network (CNN) models, which are effective in processing and analyzing sensor signal data, such as images, for classification. Data augmentation techniques, including quantum circuit-based augmentation, can be employed to improve the accuracy of CNN models. Another method involves contour analysis algorithms, which utilize convolution of the image in multiple directions and tracing procedures for object recognition. Additionally, the use of deep learning frameworks like TensorFlow and hardware platforms like Raspberry Pi can enable the implementation of low-cost automated systems for object sorting based on their features. These techniques and models contribute to the accurate detection and recognition of objects in scenes, overcoming challenges such as uncertainty, blurring, and occlusion.

How can computer vision systems be used to detect and track objects in real-time?3 answersComputer vision systems can be used to detect and track objects in real-time by utilizing various algorithms and techniques. One approach is to use machine learning algorithms such as You Only Look Once (YOLO) to detect objects by predicting bounding boxes and finding class probabilities for these boxes. Another method involves the use of object segmentation tools and neural networks to create an intelligent object classification system. Additionally, the combination of autoencoders and long-term short-term memory (LSTM) can be used to detect abnormal events from surveillance systems by detecting objects in video sequences. Real-time object tracking can be achieved by developing software applications that utilize pixel characteristics and employ noise and size filtering techniques. These approaches enable computer vision systems to accurately detect and track objects in real-time for various applications.

What is Object Detection in Computer Vision?5 answersObject detection in computer vision is a fundamental task that involves identifying and localizing specific objects in images or videos. It goes beyond simple object classification and provides a better understanding of the objects present. Object detection has various applications, including pose estimation, object tracking, instance segmentation, and inventory management in retail. Traditional methods like Viola-Jones, SIFT, and histogram of oriented gradients do not require historical data for training and are unsupervised, while modern deep learning networks like CNN, RCNN, YOLO, ResNet, RetinaNet, and MANet are supervised and efficient for object detection. Deep learning-based object detection models still face challenges in accurately detecting small-scale objects, but techniques like multi-scale feature fusion and candidate box refinement have shown promising results. Object detection is crucial in various machine vision applications, including surveillance, navigation, and object identification. The recent advancements in deep learning and CNNs have significantly improved the performance of object detection techniques.

How to Train an object detection?7 answers

How to detect an object in an image?7 answers

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