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Aiming at the problem of fall behavior detection in indoor surveillance video, a real-time fall behavior detection algorithm based on improved YOLOv7 network model was proposed. The target detection model based on YOLOv7 traditionally uses strided convolution to realize the feature of downsampling, but this may make the feature of the target information fuzzy. To solve this problem, a new downsampling module, robust feature downsampling, is introduced to improve the clarity of target information features during downsampling. In addition, by introducing CoordAttention attention mechanism in the concat portion of the network, the spliced feature graphs can be better merged. The experimental results show that the improved YOLOv7 model has a high accuracy and detection performance in fall behavior detection, with the accuracy reaching 98.88%, the mAP50 value reaching 98.83%, and the mAP50-95 value reaching 74.12%.This means that the algorithm can accurately detect the fall behavior of the elderly, and the family can find it in time so that the necessary rescue measures can be taken in time.