SPADE: Scalar Product Accelerator by Integer Decomposition for Object Detection

We propose a method for accelerating computation of an object detector based on a linear classifier when objects are expressed by binary feature vectors. Our key idea is to decompose a real-valued weight vector of the linear classifier into a weighted sum of a few ternary basis vectors so as to preserve the original classification scores. Our datadependent decomposition algorithm can approximate the original classification scores by a small number of the ternary basis vectors with an allowable error. Instead of using the original real-valued weight vector, the approximated classification score can be obtained by evaluating the few inner products between the binary feature vector and the ternary basis vectors, which can be computed using extremely fast logical operations. We also show that each evaluation of the inner products can be cascaded for incorporating early termination. Our experiments revealed that the linear filtering used in a HOG-based object detector becomes 36.9× faster than the original implementation with 1.5% loss of accuracy for 0.1 false positives per image in pedestrian detection task.