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Aiming at the problem of large calculation and long time consumption for the validity detection of video surveillance in factories. The dataset is established based on the factory surveillance video, and the model of hand-held probe is trained; K-Nearest-Neighbors (KNN) algorithm is applied to analyze the continuous frames of video and separate the foreground and background for the video frame, and then the foreground image of the hand-held probe can be obtained; Compare with the actual position for the probe need to be detected, the validity for detection will be evaluated. Experiment results show that the average accuracy for the validity detection in the video surveillance is about 93.26%, the recall rate is about 81.11%, and the F1 score is about 86.76% with the detection speed of 9.66 fps/s, which achieves the validity detection of the probe in video surveillance.