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This paper proposes a vision based novel approach for real-time object counting. The proposed approach uses the textural information for object counting. Speeded up robust features (SURF) are used to extract the textural information from the image. Firstly, the approach selects stable SURF features from prototype image, i.e., object of interest. These features are matched with the SURF features of scene image captured using vision interface. Feature grid vectors (FGVs) and feature grid clusters (FGCs) are formed for matched SURF features in the scene to indicate the presence of object. Support vector machine (SVM) learning is used to identify true instances of the object. A parameter tuning approach is used to find optimised heuristics for more accuracy and less computation. The proposed approach performs well irrespective of illumination, rotation and scale. A run time environment of the proposed approach is also developed to get real-time status of the object count.

This paper proposes a novel enhancement approach using Z-score for images captured under hazy and non-uniform illumination conditions for multimedia applications. The proposed approach aims to estimate the scene transmission using Z-score-based weighting function and global atmospheric light for image dehazing. On the contrary, the proposed approach equalizes the illumination channel using Z-score weighting function of non-uniformly illuminated image. The comparative analysis to show the effectiveness of the proposed approach is also presented quantitatively and visually. The datasets used for comparison are realistic single image dehazing dataset, high dynamic range dataset, images captured with commercial DIgital CaMeras, MiddleBury Stereo dataset, and natural benchmarked images. The dehazed images are compared in terms of peak signal to noise ratio, structural similarity index, Lightness Order Error (LOE), and Naturalness Image Quality Evaluator (NIQE). The enhanced version of non-uniformly illuminated images is compared in terms of LOE and NIQE performance measures. The comparison shows that the proposed approach outperforms others.

Mobile Backhauling provides an interface between radio controller and base stations, mostly realized with a physical medium such as optical fibers or microwave radio links. With the huge mobile traffic due to an increase in mobile subscribers as well as deployment of 4G and 5G cellular network technologies, better solutions for capacity and coverage should be provided in order to enhance spectral efficiency. For 4G cellular networks, mobile backhaul networks deal with capacity, availability, deployment cost, and long‐distance reaches. In addition, mobile backhaul networks based on the 5G network incurs additional challenges that include 1 ms or less ultralow latency time requirements and ultra‐dense nature of the network capabilities. Therefore, for 5G technologies, latency delay, QoS, packet efficiency, noise suppression, and mitigation techniques, efficient modulation schemes, and packet network timing synchronization are some aspects that are to be dealt with while designing efficient backhaul approaches (wired/wireless). Current backhaul systems typically use cost‐effective solutions (eg, ‐Wi‐Fi and WiMAX)‐based packet‐switched technologies, especially Ethernet/Internet technologies and high‐speed optical fiber links. This article presents a comprehensive report on existing and recent backhaul networks especially current backhaul system, which typically uses cost‐effective solutions (e.g.‐Wi‐Fi and Wi‐MAX), based packet‐switched technologies, Ethernet/Internet technologies and high‐speed optical fiber links. This study presents millimeter wave and free space optics networks as optimal backhaul solutions for 5G technologies. The conducted survey incorporates comparison of all the technological generations and finally concludes with potential trends and performances matrices of 6G future wireless technologies.