<p dir="ltr">Practical video analytics systems that perform computer vision tasks are widely used in critical real-world scenarios such as autonomous driving and public safety. These end-to-end systems sequentially perform tasks like object detection, segmentation, and recognition such that the performance of each analytics task depends on how well the previous tasks are performed. Typically, these systems are deployed in resources and bandwidth-constrained environments, so video compression algorithms like HEVC are necessary to minimize transmission bandwidth at the expense of input quality. Furthermore, to optimize resource utilization of these systems, the analytics tasks should be executed solely on inputs that may provide valuable insights on task performance. Hence, it is essential to understand the impact of compression and input data quality on the overall performance of end-to-end video analytics systems, using meaningfully curated datasets and interpretable evaluation procedures. This information is crucial for the overall improvement of system performance. Thus, in this thesis we focus on:</p><ol><li>Understanding the effects of compression on the performance of video analytics systems that perform tasks such as pedestrian detection, face detection, and face recognition. With this, we develop a task-aware video encoding strategy for HEVC that improves system performance under compression.</li><li>Designing methodologies to perform a meaningful and interpretable evaluation of an end-to-end system that sequentially performs face detection, alignment, and recognition. This involves balancing datasets, creating consistent ground truths, and capturing the performance interdependence between the various tasks of the system.</li><li>Estimating how image quality is linked to task performance in end-to-end face analytics systems. Here, we design novel task-aware image Quality Estimators (QEs) that determine the suitability of images for face detection. We also propose systematic evaluation protocols to showcase the efficacy of our novel face detection QEs and existing face recognition QEs. </li></ol><p dir="ltr"><br></p>