Energy-efficient Object Detection and Tracking on Embedded Smart Cameras by Hardware-level Operations at the Image Sensor

Embedded smart cameras have limited processing power, memory and energy. In this paper, we introduce two methodologies to increase the energy-efficiency and the battery-life of an embedded smart camera by hardware-level operations when performing object detection and tracking. We use the CITRIC platform as our embedded smart camera. We first perform down-sampling at hardware-level on the microcontroller of the image sensor rather than performing software-level down-sampling at the main microprocessor of the camera board. In addition, instead of performing object detection on whole image, we first estimate the location of the target in the next frame, form a search region around it, then crop the next frame by using the HREF and VSYNC signals at the microcontrollerof the image sensor, and perform detection and tracking only in the cropped search region. Thus, the amount of data that is moved from the image sensor to the main memory at each frame, is greatly reduced. Thanks to reduced data transfer, better use of the memory resources and not occupying the main microprocessor with image down-sampling and cropping tasks, we obtain significant savings in energy consumption and battery-life. Experimental results show that hardware-level down-sampling and cropping, and performing detection in cropped regions provide 54:14% decrease in energy consumption, and 121:25% increase in battery-life compared to performing software-level downsampling and processing whole frame

Towards Verification of the {Pastry} Routing Protocol using {TLA+}

International audiencePastry is an algorithm that provides a scalable distributed hash table over an underlying P2P network. Several implementations of Pastry are available and have been applied in practice, but no attempt has so far been made to formally describe the algorithm or to verify its properties. Since Pastry combines rather complex data structures, asynchronous communication, concurrency, resilience to churn and fault tolerance, it makes an interesting target for verification. We have modeled Pastry's core routing algorithms and communication protocol in the specification language TLA+. In order to validate the model and to search for bugs we employed the TLA+ model checker TLC to analyze several qualitative properties. We obtained non-trivial insights in the behavior of Pastry through the model checking analysis. Furthermore, we started to verify Pastry using the very same model and the interactive theorem prover TLAPS for TLA+. A first result is the reduction of global Pastry correctness properties to invariants of the underlying data structures

A Real-Time Prototype for Small-Vocabulary Audio-Visual ASR

We present a prototype for the automatic recognition of audiovisual speech, developed to augment the IBM ViaVoice TM speech recognition system. Frontal face, full frame video is captured through a USB 2.0 interface by means of an inexpensive PC camera, and processed to obtain appearance-based visual features. Subsequently, these are combined with audio features, synchronously extracted from the acoustic signal, using a simple discriminant feature fusion technique. On the average, the required computations utilize approximately 67 % of a Pentium TM 4, 1.8 GHz processor, leaving the remaining resources available to hidden Markov model based speech recognition. Real-time performance is therefore achieved for small-vocabulary tasks, such as connected-digit recognition. In the paper, we discuss the prototype architecture based on the ViaVoice TM engine, the basic algorithms employed, and their necessary modifications to ensure real-time performance and causality of the visual front end processing. We benchmark the resulting system performance on stored videos against prior research experiments, and we report a close match between the two. 1