Object detection is the key process in any video surveillance application. In case of remote surveillance, it is a necessity to accurately detect the target and transmit the detected data rapidly to main station so that further actions can be taken. This paper concentrates on a framework which uses deep neural network and Internet of Things for target detection and transferring detected information to the cloud at low transmission rates. The detection framework is based on combination of YOLO-Lite which is a simpler version of you only look once (YOLO) detector and spatial pyramid pooling (SPP). When trained on COCO dataset, YOLO-Lite + SPP model runs at a speed of 40 fps with mAP of 35.7% on non-GPU platform. Performance of the same has been analyzed on PASCAL VOC, COCO, TB-50 and TB-100 dataset. On GPU based platform, precision and recall values of 89.79% and 91.67% has been achieved with processing speed of 218 fps. ThingSpeak platform has been used for data reception on cloud. Results in real-time are also demonstrated which proves the efficiency of the anticipated framework and also confirms its suitability for remote video surveillance.

对象检测是任何视频监控应用程序中的关键过程。在远程监视的情况下，必须准确地检测目标并将检测到的数据快速传输到主站，以便可以采取进一步的措施。本文着重于一个框架，该框架使用深度神经网络和物联网进行目标检测，并以低传输速率将检测到的信息传输到云中。该检测框架基于YOLO-Lite的组合，YOLO-Lite是仅查看一次（YOLO）检测器和空间金字塔池（SPP）的简单版本。在COCO数据集上训练时，YOLO-Lite + SPP模型在非GPU平台上以40 fps的速度运行，mAP为35.7％。已在PASCAL VOC，COCO，TB-50和TB-100数据集上分析了其性能。在基于GPU的平台上，218 fps的处理速度实现了89.79％和91.67％的精度和召回率。ThingSpeak平台已用于云上的数据接收。还显示了实时结果，证明了预期框架的效率，并确认了其适用于远程视频监控。