In the Artificial Intelligence of Things (AIoT) era, always-on intelligent and self-powered visual perception systems have gained considerable attention and are widely used. Thus, this paper proposes TizBin, a low-power processing in-sensor scheme with event and object detection capabilities to eliminate power costs of data conversion and transmission and enable data-intensive neural network tasks. Once the moving object is detected, TizBin architecture switches to the high-power object detection mode to capture the image. TizBin offers several unique features, such as analog convolutions enabling low-precision ternary weight neural networks (TWNN) to mitigate the overhead of analog buffer and analog-to-digital converters. Moreover, TizBin exploits non-volatile magnetic RAMs to store NN's weights, remarkably reducing static power consumption. Our circuit-to-application co-simulation results for TWNNs demonstrate minor accuracy degradation on various image datasets, while TizBin achieves a frame rate of 1000 and efficiency of ∼1.83 TOp/s/W.