Krishna Mohan A, Reddy PVN and Satya Prasad K
In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best…
Abstract
Purpose
In the community of visual tracking or object tracking, discriminatively learned correlation filter (DCF) has gained more importance. When it comes to speed, DCF gives the best performance. The main objective of this study is to anticipate the object visually. For tracking the object visually, the authors proposed a new model based on the convolutional regression technique. Features like HOG & Harris are used for the process of feature extraction. The proposed method will give the best results when compared to other existing methods.
Design/methodology/approach
This paper introduces the concept and research status of tracks; later the authors focus on the representative applications of deep learning in visual tracking.
Findings
Better tracking algorithms are not mentioned in the existing method.
Research limitations/implications
Visual tracking is the ability to control eye movements using the oculomotor system (vision and eye muscles working together). Visual tracking plays an important role when it comes to identifying an object and matching it with the database images. In visual tracking, deep learning has achieved great success.
Practical implications
The authors implement the multiple tracking methods, for better tracking purpose.
Originality/value
The main theme of this paper is to review the state-of-the-art tracking methods depending on deep learning. First, we introduce the visual tracking that is carried out manually, and secondly, we studied different existing methods of visual tracking based on deep learning. For every paper, we explained the analysis and drawbacks of that tracking method. This paper introduces the concept and research status of tracks, later we focus on the representative applications of deep learning in visual tracking.
Details
Keywords
Wuletaw Tadesse, Zewdie Bishaw and Solomon Assefa
This paper aims to review the current status of wheat production, farming systems, production constraints and wheat demand-supply chain analysis; the role of international and…
Abstract
Purpose
This paper aims to review the current status of wheat production, farming systems, production constraints and wheat demand-supply chain analysis; the role of international and national breeding programs and their approaches in wheat genetic improvement including targeting mega environments, shuttle breeding, doubled haploids, marker-assisted selection and key location phenotyping; and future prospects and opportunities of wheat production in Sub Saharan Africa (SSA).
Design/methodology/approach
Relevant literature works have been used and cited accordingly.
Findings
Though traditionally wheat was not the leading staple crop in SSA, it is becoming an important food crop because of rapid population growth associated with increased urbanization and change in food preference for easy and fast food such as bread, biscuits, pasta, noodles and porridge. In 2013, total wheat consumption in SSA reached 25 million tons with import accounting for 17.5 million tons at a price of USD6 billion, while during the same period the region produces only 7.3 million tons on a total area of 2.9 million hectares. The low productivity (2t/ha) in the region is principally because of abiotic (drought and heat) and biotic (yellow rust, stem rust, septoria and fusarium) stresses which are increasing in intensity and frequency associated with climate change. Furthermore, increased cost of production, growing populations, increased rural-urban migration, low public and private investments, weak extension systems and policies, and low adoption rates of new technologies remain to be major challenges for wheat production in SSA. Wheat breeding in SSA is dominantly carried out by National Agricultural Research Systems, in partnership with the international research centers [International center for improvement of maize and wheat (CIMMYT) and International center for agricultural research in the dry areas (ICARDA)], to develop high yielding and widely adapted wheat genotypes with increased water-use efficiency, heat tolerance and resistance to major diseases and pests. Most of the cultivars grown in SSA are originated from the international research centers, CIMMYT and ICARDA.
Practical implications
This paper will help to promote available wheat technologies in SSA by creating awareness to wheat scientists, extension agents and policymakers.
Originality/value
This manuscript is an original review paper which has not been published in this form elsewhere.