Offline digital advertising has opened up new avenues of promotion for businesses. The advantages of offline advertising can be realized more effectively if user targeting is done, similar to online advertising. The previous research, proposed by Malhotra et al. (International Journal of Engineering and Advanced Technology 2020 [1]), presents a technology that uses recognition-based demographics and queue scheduling for user targeted offline advertising. The technology uses cameras on billboards to collect demographics of the viewing population and quantitatively finds the centroid to show the most appropriate advertisement on every billboard. This paper proposes an improved demographic generation technique which uses an enhanced convolutional neural network (CNN) and deviation-based scheduling to display the advertisements on billboards. Deviation-based scheduling helps in improving coverage and increases the relevant viewership of the advertisement. Also, a higher deviation reduces the distance between static advertisement coordinates and dynamic billboard coordinates. This distance represents suitability of the advertisements on the respective billboard. Higher deviation allows advertisers to promote among rare target groups, prevents the overlapping target audience problem, and offers high returns on investment. The earlier multimedia and intelligent system was named Target. Advertise. Revolutionize. Promote. (T.A.R.P.). This multimedia system shall be referred to as T.A.R.P. 2.0 throughout the paper. T.A.R.P. 2.0. is more potent than T.A.R.P., as it is optimized, highly scalable, and handles several cases that were beyond the scope of T.A.R.P.

Offline advertisements are static in nature. Advertising companies use billboards for advertising. These billboards display advertisements in a random fashion depending on the investment made by the advertiser. Advertisers pay a fixed amount of money for displaying their advertisements and not on the basis of relevant viewership. The technology proposed in the paper ensures that this disparity is handled wherein offline advertisements are targeted to the relevant audience. The technology has been named TARP which is an abbreviation for Target. Advertise. Revolutionise. Promote. TARP uses built in cameras on offline advertising platforms such as billboards & TV Screens in malls, restaurants, metro & airports to target advertisements based on gender, age and other relevant demographics. The technology is a boon for the advertising industry and benefits both advertisers and viewers. It displays what viewers want to see and who the advertisers want to reach out to. Convolutional neural networks are used to generate demographics of viewing population. Centroids of the viewing population are maintained for each billboard. Advertisements search for the most relevant billboard for display. Display of advertisements is monitored by a queue scheduling algorithm. The research paper proposes an algorithm to generate demographics, search most relevant billboard for each advertisement as well as generate priority queues.

Under foggy conditions, visibility decreases and causes many problems. Less visibility due to foggy conditions while driving increases the risk of road accidents. It is important to detect and recognize the nearby objects under such conditions and predict the distance of collision. There is a need to devise an object detection mechanism during foggy conditions. The paper proposes a solution to this problem by proposing a VESY(Visibility Enhancement Saliency YOLO) sensor which uses an algorithm that fuses the saliency map of the foggy image frame with the output generated from object detection algorithm YOLO (You Only Look Once). The image is sensed using image sensors in a stereo camera which are activated using a fog sensor and a depth map is generated to calculate the distance of collision. Dehaze algorithm is applied to improve the quality of the image frame whose Saliency image is generated on the basis of region covariance matrix. YOLO algorithm is also implemented on the improved quality image. The proposed fusion algorithm gives the bounding boxes of the union of the objects detected by Saliency Map and YOLO Algorithm thus proving to be a viable solution for real-time applications.