Activity detection in conversational sign language video for mobile telecommunication

The goal of the MobileASL project is to increase accessibility by making the mobile telecommunications network available to the signing Deaf community. Video cell phones enable Deaf users to communicate in their native language, American Sign Language (ASL). However, encoding and transmission of real-time video over cell phones is a powerintensive task that can quickly drain the battery. By recognizing activity in the conversational video, we can drop the frame rate during less important segments without significantly harming intelligibility, thus reducing the computational burden. This recognition must take place from video in real-time on a cell phone processor, on users that wear no special clothing. In this work, we quantify the power savings from droppin

Variable Frame Rate for Low Power Mobile Sign Language Communication

The MobileASL project aims to increase accessibility by enabling Deaf people to communicate over video cell phones in their native language, American Sign Language (ASL). Real-time video over cell phones can be a computationally intensive task that quickly drains the battery, rendering the cell phone useless. Properties of conversational sign language allow us to save power and bits: namely, lower frame rates are possible when one person is not signing due to turntaking, and signing can potentially employ a lower frame rate than fingerspelling. We conduct a user study with native signers to examine the intelligibility of varying the frame rate based on activity in the video. We then describe several methods for automatically determining the activity of signing or not signing from the video stream in real-time. Our results show that varying the frame rate during turn-taking is a good way to save power without sacrificing intelligibility, and that automatic activity analysis is feasible

Dynamic pricing for impatient bidders

We study the following problem related to pricing over time. Assume there is a collection of bidders, each of whom is interested in buying a copy of an item of which there is an unlimited supply. Every bidder is associated with a time interval over which the bidder will consider buying a copy of the item, and a maximum value the bidder is willing to pay for the item. On every time unit, the seller sets a price for the item. The seller's goal is to set the prices so as to maximize revenue from the sale of copies of items over the time period. In the first model considered, we assume that all bidders are impatient, that is, bidders buy the item at the first time unit within their bid interval that they can afford the price. To the best of our knowledge, this is the first work that considers this model. In the offline setting, we assume that the seller knows the bids of all the bidders in advance. In the online setting we assume that at each time unit the seller only knows the values of the bids that have arrived before or at that time unit. We give a polynomial time offline algorithm and prove upper and lower bounds on the competitiveness of deterministic and randomized online algorithms, compared with the optimal offline solution. The gap between the upper and lower bounds is quadratic. We also consider the envy-free model in which bidders are sold the item at the minimum price during their bid interval, as long as it is not over their limit value. We prove tight bounds on the competitiveness of deterministic online algorithms for this model, and upper and lower bounds on the competitiveness of randomized algorithms with quadratic gap. The lower bounds for the randomized case in both models use a novel general technique

Activity Analysis Enabling Real-Time Video Communication on Mobile Phones for Deaf Users

We describe our system called MobileASL for real-time video communication on the current U.S. mobile phone network. The goal of MobileASL is to enable Deaf people to communicate with Sign Language over mobile phones by compressing and transmitting sign language video in real-time on an off-the-shelf mobile phone, which has a weak processor, uses limited bandwidth, and has little battery capacity. We develop several H.264-compliant algorithms to save system resources while maintaining ASL intelligibility by focusing on the important segments of the video. We employ a dynamic skin-based region-of-interest (ROI) that encodes the skin at higher quality at the expense of the rest of the video. We also automatically recognize periods of signing versus not signing and raise and lower the frame rate accordingly, a technique we call variable frame rate (VFR). We show that our variable frame rate technique results in a 47 % gain in battery life on the phone, corresponding to an extra 68 minutes of talk time. We also evaluate our system in a user study. Participants fluent in ASL engage in unconstrained conversations over mobile phones in a laboratory setting. We find that the ROI increases intelligibility and decreases guessing. VFR increases the need for signs to be repeated and the number of conversational breakdowns, but does not affect the users ’ perception of adopting the technology. These results show that our sign language sensitive algorithms can save considerable resources without sacrificing intelligibility. ACM Classification: H5.2 [Information interfaces and presentation]:Multimedia Information Systems–Video. K.4.2 [Computers and Society]: Social Issues–Assistive technologies for persons with disabilities. General terms

Abstract Dynamic Pricing for Impatient Bidders

We study the following problem related to pricing over time. Assume there is a collection of bidders, each of whom is interested in buying a copy of an item of which there is an unlimited supply. Every bidder is associated with a time interval over which the bidder will consider buying a copy of the item, and a maximum value the bidder is willing to pay for the item. On every time unit the seller sets a price for the item. The seller’s goal is to set the prices so as to maximize revenue from the sale of copies of items over the time period. In the first model considered we assume that all bidders are impatient, that is, bidders buy the item at the first time unit within their bid interval that they can afford the price. To the best of our knowledge, this is the first work that considers this model. In the offline setting we assume that the seller knows the bids of all the bidders in advance. In the online setting we assume that at each time unit the seller only knows the values of the bids that have arrived before or at that time unit. We give a polynomial time offline algorithm and prove upper and lower bounds on the competitiveness of deterministic and randomized online algorithms, compared with the optimal offline solution. The gap between the upper and lower bounds is quadratic. We also consider the envy free model in which bidders are sold the item at the minimum price during their bid interval, as long as it is not over their limit value. We prove tight bounds on the competitiveness of deterministic online algorithms for this model, and upper and lower bounds on the competitiveness of randomized algorithms with quadratic gap. The lower bounds for the randomized case in both models uses a novel general technique