An important area of computer vision is real-time object tracking, which is now widely used in intelligent transportation and smart industry technologies. Although the correlation filter object tracking methods have a good real-time tracking effect, it still faces many challenges such as scale variation, occlusion, and boundary effects. Many scholars have continuously improved existing methods for better efficiency and tracking performance in some aspects. To provide a comprehensive understanding of the background, key technologies and algorithms of single object tracking, this article focuses on the correlation filter-based object tracking algorithms. Specifically, the background and current advancement of the object tracking methodologies, as well as the presentation of the main datasets are introduced. All kinds of methods are summarized to present tracking results in various vision problems, and a visual tracking method based on reliability is observed.

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Overview and methods of correlation filter algorithms in object tracking

Stochastic configuration network (SCN), as a novel incremental generation model with supervisory mechanism, has an excellent superiority in solving large-scale data regression and classification problems. However, the accuracy of the SCN is affected by the assignation and selection of some network parameters significantly Sparrow search algorithm (SSA) is a new meta-heuristic algorithm that simulates the foraging and anti-predation behavior of sparrow population. In this paper, a stochastic configuration network based on chaotic sparrow search algorithm is first introduced, termed as CSSA-SCN. Firstly, chaotic sparrow search algorithm (CSSA) is designed which mainly utilizes logistic mapping, self-adaptive hyper-parameters, mutation operator to enhance the global optimization capability of SSA; Secondly, as the performance of SCN is related to regularization parameter r and scale factor λ of weights and biases, then CSSA is employed to give better parameters for SCN automatically; Finally, 13 benchmark functions and several datasets are used to evaluate the performance of CSSA and CSSA-SCN respectively. Experimental results demonstrate the feasibility and validity of CSSA-SCN compared with SCN and other contrast algorithms.

A stochastic configuration network based on chaotic sparrow search algorithm

The massive amount of video data produced by surveillance networks in industries instigate various challenges in exploring these videos for many applications, such as video summarization (VS), analysis, indexing, and retrieval. The task of multiview video summarization (MVS) is very challenging due to the gigantic size of data, redundancy, overlapping in views, light variations, and interview correlations. To address these challenges, various low-level features and clustering-based soft computing techniques are proposed that cannot fully exploit MVS. In this article, we achieve MVS by integrating deep neural network based soft computing techniques in a two-tier framework. The first online tier performs target-appearance-based shots segmentation and stores them in a lookup table that is transmitted to cloud for further processing. The second tier extracts deep features from each frame of a sequence in the lookup table and pass them to deep bidirectional long short-term memory (DB-LSTM) to acquire probabilities of informativeness and generates a summary. Experimental evaluation on benchmark dataset and industrial surveillance data from YouTube confirms the better performance of our system compared to the state-of-the-art MVS methods.

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Cloud-Assisted Multiview Video Summarization Using CNN and Bidirectional LSTM

Agriculture is the basis of every economy worldwide. Crop production is one of the major factors affecting domestic market condition in any country. Agricultural production is also a major prerequisite of economic development, be it any part of any country. It plays a crucial role as it even provides raw material, employment and food to different citizens. A lot of issues are responsible for estimated crop production varying in different parts of the world. Some of these include overutilization of chemical fertilizers, presence of chemicals in water supply, uneven distribution of rainfall, different soil fertility and others. Other than these issues one of the commonly faced challenges across the globe equally includes destruction of the major part of production due to diseases. After providing effective resources to the fields, major section of the production is diminished by the presence of diseases in the plants grown. This leads to focus on effective ways of detection of disease in plants. Presence of various diseases in plant is a major concern among farmers. Plant diseases acts as a major threat to small scale farmers as they lead to major destruction in overall food supply. To provide effective measures for detection and avoidance of the destruction requires an early identification of type of plant disease present. In recent time major work is being done for the identification of plant disease presents in varied parts of the world affection varied crops. Major work is being done in the domain of identification of causing factors of these diseases. Some of the diseases are marked by the presence of viruses while some are resultant of fungal infection. This becomes a major issue when the causing factor is not traceable before it has already spread to major production section. This paper brings a review on effective use of different imaging techniques and computer vision approaches for the identification and classification of plant diseases. Detection of Plant disease is initiated with image acquisition followed by pre-processing while using the process of segmentation. It is further accompanied by different techniques used for feature extraction along with classification. In this Paper we present the Current Trends and Challenges for detection of plant disease using computer vision and advance imaging technique.

A review of imaging techniques for plant disease detection

Meta-learning baselines and database for few-shot classification in agriculture

One of the ultimate key factors besttow less yield is disease attack. The groundnut plant disease such as fungi, soil borne and viruses. In this paper, we are given that software determination to robotically classify and categorize groundnut leaf diseases. This method will improve production of crops. It comprises of number of steps viz. image acquisition, image pre-processing, segmentation, features extraction and classifier using K Nearest Neighbor (KNN). To increase performance of existing algorithm the SVM classifier is replaced with KNN classification. In this paper we categorized only 4 different disease using KNN classifier algorithm.

Detection and Classification of Groundnut Leaf Diseases using KNN classifier

With the advancement in digital video technology, video surveillance has been playing its vital role for ensuring safety and security. The surveillance systems are deployed in wide range of applications to invigilate stuffs and to analyse the activities in the environment. From the single or multi surveillance camera, a huge amount of data is generated, stored and processed for security purpose. Due to time constraints, it is a very tedious process for an analyst to go through the full content. This limitation has been overcome by the use of video summarization. The video summarization is intended to afford comprehensible analysis of video by removing duplications and extracting key frames from the video. To make an easily interpreted outline, the various available video summarization methods will try to shot the summary of the main occurrences, scenes, or objects in a frame. Depending on the applications, it is required to summarize the happenings in the scene and detect the objects (static/dynamic) which is recorded in the video. Hence this paper provides the various methods used for video summarization and a comparative study of different techniques. It also presents different object detection, object classification and object tracking algorithms available in the literature.

A study on various methods used for video summarization and moving object detection for video surveillance applications

This research aims to identify rice diseases, namely Leaf blast, Brown spot, Healthy and Hispa. The purpose of this research is to utilize deep convolutional neural network (DCNN) with support vector machine (SVM), DCNN with artificial neural network (ANN) and DCNN with long short-term memory (LSTM). To enhance the performance of LSTM further, the research includes particle swarm optimization, artificial fish swarm optimization (AFSO) and efficient artificial fish swarm optimization (EAFSO) to identify optimal weights. This research also compares the proposed technique results with a conventional feature extraction approaches like texture, discrete wavelet transforms and color histogram with SVM, ANN and LSTM.
 The results exhibit the superiority of proposed DCNN-LSTM (EAFSO) technique over other techniques. The proposed technique EAFSO associates DCNN-LSTM identifies the rice diseases with 97.5% accuracy, which is better than DCNN-SVM and DCNN-ANN.

Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases

A machine learning approach for detecting and tracking road boundary lanes

Groundnut is one of the most important and popular oilseed foods in the agricultural field, and its botanical name is Arachis hypogaea L. Approximately, the pod of mature groundnut contains 1–5 seeds with 57% of oil and 25% of protein content. The oil obtained from the groundnut is widely used for cooking and losing body weight, and its fats are widely used for making soaps. The groundnut cultivation is affected by different kinds of diseases such as fungi, viruses, and bacteria. Hence, these diseases affect the leaf, root and stem of the groundnut plant and it leads to heavy loss in yield. Moreover, the enlarger number of diseases affects the leaf and root-like Alternaria, Pestalotiopsis, Bud necrosis, tikka, Phyllosticta, Rust, Pepper spot, Choanephora, early and late leaf spot. To overcome these issues, we introduce an efficient method of deep convolutional neural network (DCNN) because it automatically detects the important features without any human supervision. The DCNN procedure can deeply detect plant disease by using a deep learning process. Moreover, the DCNN training and testing process demonstrate an accurate groundnut disease determination and classification result. The number of groundnut leaf disease images is chosen from the plant village dataset, and it is used for the training and testing process. The stochastic gradient decent momentum method is used for dataset training, and it has shown the better performance of proposed DCNN. From the comparison analysis, the 6th combined layer of proposed DCNN delivers a 95.28% accuracy value. Ultimately, the groundnut disease classification with its overall performance of proposed DCNN provides 99.88% accuracy.

K. Suganya Devi

Papers

Detection and Classification of Groundnut Leaf Diseases using KNN classifier

A study on various methods used for video summarization and moving object detection for video surveillance applications

Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases

A machine learning approach for detecting and tracking road boundary lanes

Automatic method for classification of groundnut diseases using deep convolutional neural network