An image-based real-time intrusion detection method and a surveillance camera that use artificial intelligence are provided to sample a plurality of frames input at a first point in time, acquire a probability that at least one object corresponding to a type of a target object exists in an image of the respective sampled frames by using a first artificial neural network, adjust a sampling rate for a plurality of frames to be input at a second point in time after the first point in time according to processing time of each frame of the first artificial neural network required to acquire an existence probability of the at least one object, select each of the respective sampled frames as a frame of the target object according to a magnitude of the acquired probability, generate a movement trajectory of each object corresponding to the type of the target object from the frames selected as the frame of the target object, and acquire an intrusion occurrence probability from the generated movement trajectory by using a second artificial neural network.