Efficient Object Detection via Adaptive Online Selection of Sensor-Array Elements
Authors: Matthai Philipose
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present empirical evidence over several hundred thousand frames of temperaturegated video from a variety of day-to-day settings that shows an estimated reduction of 50x in power required to detect faces relative to RGB-only processing, at 9% reduction in detection rates. We further break out the benefits of adaptivity, online processing and our optimizations. |
| Researcher Affiliation | Industry | Matthai Philipose Microsoft |
| Pseudocode | No | The paper describes algorithms and formulations using mathematical equations and descriptive text, but it does not include a clearly labeled pseudocode or algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statements about making its source code open or available, nor does it provide a link to a code repository. |
| Open Datasets | No | We collected QVGA (320x240) far-infrared video at 10fps with aligned VGA (640x480) RGB frames of daily life in three settings: office , walk and lobby. ... with a total 10hrs of data for each scenario. |
| Dataset Splits | No | The paper does not explicitly provide specific training/validation/test dataset splits with percentages, absolute counts, or references to predefined splits. It mentions that "A-O and A-NO are trained on office and lobby data but not walk data" but this does not constitute a formal split description. |
| Hardware Specification | No | The paper mentions power consumption estimates for "Proposed silicon implementations of the Viola-Jones algorithm" from other works but does not specify the hardware (e.g., specific GPU/CPU models) used for its own experiments. It refers to "P = 40n J to read a pixel and I = 5n J per instruction" as estimates for calculation, not the hardware used. |
| Software Dependencies | No | The paper mentions running "Viola-Jones face detection" but does not provide specific version numbers for any software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | We collected QVGA (320x240) far-infrared video at 10fps with aligned VGA (640x480) RGB frames of daily life in three settings: office , walk and lobby. ... We choose to incur cost sufficient to read and classify a few imager windows per frame: for small n, = n Cp/|W t|. ... We use the Kullback-Leibler divergence D(Pr k Pr0 ). ... we choose i such that Pr(W = 1|gi < i) 0.01 and Pr(W = 1|gi i) 0.3. |