The object recognition concept is being widely used a result of increasing CCTV surveillance and the need for automatic object or activity detection from images or video. Increases in the use of various sensor networks have also raised the need of lightweight process frameworks. Much research has been carried out in this area, but the research scope is colossal as it deals with open-ended problems such as being able to achieve high accuracy in little time using lightweight process frameworks. Convolution Neural Networks and their variants are widely used in various computer vision activities, but most of the architectures of CNN are application-specific. There is always a need for generic architectures with better performance. This paper introduces the Dimension-Based Generic Convolution Block (DBGC), which can be used with any CNN to make the architecture generic and provide a dimension-wise selection of various height, width, and depth kernels. This single unit which uses the separable convolution concept provides multiple combinations using various dimension-based kernels. This single unit can be used for height-based, width-based, or depth-based dimensions; the same unit can even be used for height and width, width and depth, and depth and height dimensions. It can also be used for combinations involving all three dimensions of height, width, and depth. The main novelty of DBGC lies in the dimension selector block included in the proposed architecture. Proposed unoptimized kernel dimensions reduce FLOPs by around one third and also reduce the accuracy by around one half; semi-optimized kernel dimensions yield almost the same or higher accuracy with half the FLOPs of the original architecture, while optimized kernel dimensions provide 5 to 6% higher accuracy with around a 10 M reduction in FLOPs.

The 'Matchmove', or camera-tracking process is a crucial task and one of the first to be performed in the visual effects pipeline. An accurate solve for camera movement is imperative and will have an impact on almost every other part of the pipeline downstream. In this work we present a comprehensive analysis of the process at a major visual effects studio, drawing on a large dataset of real shots. We also present guidelines and rules-of-thumb for camera tracking scheduling which are, in what we believe to be an industry first, backed by statistical data drawn from our dataset. We also make available data from our pipeline which shows the amount of time spent on camera tracking and the types of shot that are most common in our work. We hope this will be of interest to the wider computer vision research community and will assist in directing future research.

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