To address the dilemma of trade-off between efficiency and accuracy for object detection

based on the mechanism of object perception and recognition in visual attention theory

the two sides derived from gradient feature as magnitude and direction have been revisited to manifest their complementary characteristics.The new rapid object detection model based on two-layer cascade with gradients is motivated

making two types of category-independent and category-dependent detectors efficiently described.On the one hand

gradient magnitude can be used to generate the efficient object proposal in clutter from sliding window samples which guarantees the significant decrease on the number of windows for candidate and speeds up detection.On the other hand

the cascade-architecture in form of multiple sub-detectors can well adapt to the varying scales of different objects resulting in boost of accuracy.Experimental performance in PASCAL presents the effectiveness of cascade structure for gradient features

and demonstrates that our model can dramatically speed up the detection with the advantages of comparable accuracy against the state-of-the-art.