5,721 research outputs found
Car that Knows Before You Do: Anticipating Maneuvers via Learning Temporal Driving Models
Advanced Driver Assistance Systems (ADAS) have made driving safer over the
last decade. They prepare vehicles for unsafe road conditions and alert drivers
if they perform a dangerous maneuver. However, many accidents are unavoidable
because by the time drivers are alerted, it is already too late. Anticipating
maneuvers beforehand can alert drivers before they perform the maneuver and
also give ADAS more time to avoid or prepare for the danger.
In this work we anticipate driving maneuvers a few seconds before they occur.
For this purpose we equip a car with cameras and a computing device to capture
the driving context from both inside and outside of the car. We propose an
Autoregressive Input-Output HMM to model the contextual information alongwith
the maneuvers. We evaluate our approach on a diverse data set with 1180 miles
of natural freeway and city driving and show that we can anticipate maneuvers
3.5 seconds before they occur with over 80\% F1-score in real-time.Comment: ICCV 2015, http://brain4cars.co
Joint segmentation of multivariate time series with hidden process regression for human activity recognition
The problem of human activity recognition is central for understanding and
predicting the human behavior, in particular in a prospective of assistive
services to humans, such as health monitoring, well being, security, etc. There
is therefore a growing need to build accurate models which can take into
account the variability of the human activities over time (dynamic models)
rather than static ones which can have some limitations in such a dynamic
context. In this paper, the problem of activity recognition is analyzed through
the segmentation of the multidimensional time series of the acceleration data
measured in the 3-d space using body-worn accelerometers. The proposed model
for automatic temporal segmentation is a specific statistical latent process
model which assumes that the observed acceleration sequence is governed by
sequence of hidden (unobserved) activities. More specifically, the proposed
approach is based on a specific multiple regression model incorporating a
hidden discrete logistic process which governs the switching from one activity
to another over time. The model is learned in an unsupervised context by
maximizing the observed-data log-likelihood via a dedicated
expectation-maximization (EM) algorithm. We applied it on a real-world
automatic human activity recognition problem and its performance was assessed
by performing comparisons with alternative approaches, including well-known
supervised static classifiers and the standard hidden Markov model (HMM). The
obtained results are very encouraging and show that the proposed approach is
quite competitive even it works in an entirely unsupervised way and does not
requires a feature extraction preprocessing step
Multimodal Affect Recognition: Current Approaches and Challenges
Many factors render multimodal affect recognition approaches appealing. First, humans employ a multimodal approach in emotion recognition. It is only fitting that machines, which attempt to reproduce elements of the human emotional intelligence, employ the same approach. Second, the combination of multiple-affective signals not only provides a richer collection of data but also helps alleviate the effects of uncertainty in the raw signals. Lastly, they potentially afford us the flexibility to classify emotions even when one or more source signals are not possible to retrieve. However, the multimodal approach presents challenges pertaining to the fusion of individual signals, dimensionality of the feature space, and incompatibility of collected signals in terms of time resolution and format. In this chapter, we explore the aforementioned challenges while presenting the latest scholarship on the topic. Hence, we first discuss the various modalities used in affect classification. Second, we explore the fusion of modalities. Third, we present publicly accessible multimodal datasets designed to expedite work on the topic by eliminating the laborious task of dataset collection. Fourth, we analyze representative works on the topic. Finally, we summarize the current challenges in the field and provide ideas for future research directions
Using Generic Summarization to Improve Music Information Retrieval Tasks
In order to satisfy processing time constraints, many MIR tasks process only
a segment of the whole music signal. This practice may lead to decreasing
performance, since the most important information for the tasks may not be in
those processed segments. In this paper, we leverage generic summarization
algorithms, previously applied to text and speech summarization, to summarize
items in music datasets. These algorithms build summaries, that are both
concise and diverse, by selecting appropriate segments from the input signal
which makes them good candidates to summarize music as well. We evaluate the
summarization process on binary and multiclass music genre classification
tasks, by comparing the performance obtained using summarized datasets against
the performances obtained using continuous segments (which is the traditional
method used for addressing the previously mentioned time constraints) and full
songs of the same original dataset. We show that GRASSHOPPER, LexRank, LSA,
MMR, and a Support Sets-based Centrality model improve classification
performance when compared to selected 30-second baselines. We also show that
summarized datasets lead to a classification performance whose difference is
not statistically significant from using full songs. Furthermore, we make an
argument stating the advantages of sharing summarized datasets for future MIR
research.Comment: 24 pages, 10 tables; Submitted to IEEE/ACM Transactions on Audio,
Speech and Language Processin
Latent Class Model with Application to Speaker Diarization
In this paper, we apply a latent class model (LCM) to the task of speaker
diarization. LCM is similar to Patrick Kenny's variational Bayes (VB) method in
that it uses soft information and avoids premature hard decisions in its
iterations. In contrast to the VB method, which is based on a generative model,
LCM provides a framework allowing both generative and discriminative models.
The discriminative property is realized through the use of i-vector (Ivec),
probabilistic linear discriminative analysis (PLDA), and a support vector
machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid are introduced. In addition, three further improvements are
applied to enhance its performance. 1) Adding neighbor windows to extract more
speaker information for each short segment. 2) Using a hidden Markov model to
avoid frequent speaker change points. 3) Using an agglomerative hierarchical
cluster to do initialization and present hard and soft priors, in order to
overcome the problem of initial sensitivity. Experiments on the National
Institute of Standards and Technology Rich Transcription 2009 speaker
diarization database, under the condition of a single distant microphone, show
that the diarization error rate (DER) of the proposed methods has substantial
relative improvements compared with mainstream systems. Compared to the VB
method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and
LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments
on our collected database, CALLHOME97, CALLHOME00 and SRE08 short2-summed trial
conditions also show that the proposed LCM-Ivec-Hybrid system has the best
overall performance
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