Reliable object detection is essential for smart surveillance systems, and video surveillance technology has been significantly improved thanks to Deep-Learning related techniques. The deployment of resource-constraint edge device algorithms based on Deep-Learning objection detection is an attractive approach with a range of challenging practical issues to solve, such as the enforcement of Deep-Learning model inference reliability on inexpensive, general-purpose edge devices. In this paper, we transform the state-of-the-art convolutional Single Single Shot Detector Object-Detection algorithm into a task optimization problem to investigate whether the overall system reliability can be further improved by using an under-studied technique called semi-partitioned rate-monotonic scheduling algorithm. Unlike current non-scheduling based methods, our scheduling approach and system configuration is empirically shown to achieve state-of-the-art results, such as enhancing Real-Time video inference performance from 0.5 frame-per-second (fps) to 2fps (i.e. four-fold increase) with a recorded mean 68% improvement in video fps deviation. Our findings are important as fluctuations in video fps, hereby termed system reliability, is known to affect the detection accuracy of object detection algorithms. Finally, we discuss the results and highlight several observations that have not been explicitly addressed in current literature.