Is a Detector Only Good for Detection?

A common design of an object recognition system has two steps, a detection step followed by a foreground within-class classification step. For example, consider face detection by a boosted cascade of detectors followed by face ID recognition via one-vs-all (OVA) classifiers. Another example is human detection followed by pose recognition. Although the detection step can be quite fast, the foreground within-class classification process can be slow and becomes a bottleneck. In this work, we formulate a filter-and-refine scheme, where the binary outputs of the weak classifiers in a boosted detector are used to identify a small number of candidate foreground state hypotheses quickly via Hamming distance or weighted Hamming distance. The approach is evaluated in three applications: face recognition on the FRGC V2 data set, hand shape detection and parameter estimation on a hand data set and vehicle detection and view angle estimation on a multi-view vehicle data set. On all data sets, our approach has comparable accuracy and is at least five times faster than the brute force approach.

Bayesian Packet Loss Detection for TCP

One of TCP's critical tasks is to determine which packets are lost in the network, as a basis for control actions (flow control and packet retransmission). Modern TCP implementations use two mechanisms: timeout, and fast retransmit. Detection via timeout is necessarily a time-consuming operation; fast retransmit, while much quicker, is only effective for a small fraction of packet losses. In this paper we consider the problem of packet loss detection in TCP more generally. We concentrate on the fact that TCP's control actions are necessarily triggered by inference of packet loss, rather than conclusive knowledge. This suggests that one might analyze TCP's packet loss detection in a standard inferencing framework based on probability of detection and probability of false alarm. This paper makes two contributions to that end: First, we study an example of more general packet loss inference, namely optimal Bayesian packet loss detection based on round trip time. We show that for long-lived flows, it is frequently possible to achieve high detection probability and low false alarm probability based on measured round trip time. Second, we construct an analytic performance model that incorporates general packet loss inference into TCP. We show that for realistic detection and false alarm probabilities (as are achievable via our Bayesian detector) and for moderate packet loss rates, the use of more general packet loss inference in TCP can improve throughput by as much as 25%.

Fast Head Tilt Detection for Human-Computer Interaction

Accurate head tilt detection has a large potential to aid people with disabilities in the use of human-computer interfaces and provide universal access to communication software. We show how it can be utilized to tab through links on a web page or control a video game with head motions. It may also be useful as a correction method for currently available video-based assistive technology that requires upright facial poses. Few of the existing computer vision methods that detect head rotations in and out of the image plane with reasonable accuracy can operate within the context of a real-time communication interface because the computational expense that they incur is too great. Our method uses a variety of metrics to obtain a robust head tilt estimate without incurring the computational cost of previous methods. Our system runs in real time on a computer with a 2.53 GHz processor, 256 MB of RAM and an inexpensive webcam, using only 55% of the processor cycles.