Multi activity-multi object recognition (MAMO) is a challenging task in visual systems for monitoring, recognizing and alerting in various public places, such as universities, hospitals and airports. While both academic and commercial researchers are aiming towards automatic tracking of human activities in intelligent video surveillance using deep learning frameworks. This is required for many real time applications to detect unusual/suspicious activities like tracking of suspicious behaviour in crime events etc. The primary purpose of this paper is to render a multi class activity prediction in individuals as well as groups from video sequences by using the state-of-the-art object detector You Look only Once (YOLOv3). By optimum utilization of the geographical information of cameras and YOLO object detection framework, a Deep Landmark model recognize a simple to complex human actions on gray scale to RGB image frames of video sequences. This model is tested and compared with various benchmark datasets and found to be the most precise model for detecting human activities in video streams. Upon analysing the experimental results, it has been observed that the proposed method shows superior performance as well as high accuracy.

多活动多对象识别（MAMO）是视觉系统中一项具有挑战性的任务，用于在各种公共场所（例如大学，医院和机场）进行监视，识别和警报。虽然学术研究人员和商业研究人员都致力于使用深度学习框架自动跟踪智能视频监控中的人类活动。这对于许多实时应用程序来说是必需的，以检测异常/可疑活动，例如跟踪犯罪事件中的可疑行为等。本文的主要目的是通过使用视频序列对视频序列中的个人和群体进行多类活动预测。最新的物体检测器，您只看一次（YOLOv3）。通过最佳利用摄像机的地理信息和YOLO对象检测框架，Deep Landmark模型可识别从灰度到视频序列的RGB图像帧的简单到复杂的人类动作。经过测试，该模型与各种基准数据集进行了比较，它是检测视频流中人类活动的最精确模型。通过对实验结果的分析，可以看出该方法具有较高的性能和较高的精度。