Author name cluster

Trisha Mittal

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5 papers

2 author rows

IROS Conference 2020 Conference Paper

CMetric: A Driving Behavior Measure using Centrality Functions

Rohan Chandra
Uttaran Bhattacharya
Trisha Mittal
Aniket Bera
Dinesh Manocha

We present a new measure, CMetric, to classify driver behaviors using centrality functions. Our formulation combines concepts from computational graph theory and social traffic psychology to quantify and classify the behavior of human drivers. CMetric is used to compute the probability of a vehicle executing a driving style, as well as the intensity used to execute the style. Our approach is designed for realtime autonomous driving applications, where the trajectory of each vehicle or road-agent is extracted from a video. We compute a dynamic geometric graph (DGG) based on the positions and proximity of the road-agents and centrality functions corresponding to closeness and degree. These functions are used to compute the CMetric based on style likelihood and style intensity estimates. Our approach is general and makes no assumption about traffic density, heterogeneity, or how driving behaviors change over time. We present an algorithm to compute CMetric and demonstrate its performance on real-world traffic datasets. To test the accuracy of CMetric, we introduce a new evaluation protocol (called "Time Deviation Error") that measures the difference between human prediction and the prediction made by CMetric.

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ICRA Conference 2020 Conference Paper

GraphRQI: Classifying Driver Behaviors Using Graph Spectrums

Rohan Chandra
Uttaran Bhattacharya
Trisha Mittal
Xiaoyu Li
Aniket Bera
Dinesh Manocha

We present a novel algorithm (GraphRQI) to identify driver behaviors from road-agent trajectories. Our approach assumes that the road-agents exhibit a range of driving traits, such as aggressive or conservative driving. Moreover, these traits affect the trajectories of nearby road-agents as well as the interactions between road-agents. We represent these inter-agent interactions using unweighted and undirected traffic graphs. Our algorithm classifies the driver behavior using a supervised learning algorithm by reducing the computation to the spectral analysis of the traffic graph. Moreover, we present a novel eigenvalue algorithm to compute the spectrum efficiently. We provide theoretical guarantees for the running time complexity of our eigenvalue algorithm and show that it is faster than previous methods by 2 times. We evaluate the classification accuracy of our approach on traffic videos and autonomous driving datasets corresponding to urban traffic. In practice, GraphRQI achieves an accuracy improvement of up to 25% over prior driver behavior classification algorithms. We also use our classification algorithm to predict the future trajectories of road-agents.

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AAAI Conference 2020 Conference Paper

M3ER: Multiplicative Multimodal Emotion Recognition using Facial, Textual, and Speech Cues

Trisha Mittal
Uttaran Bhattacharya
Rohan Chandra
Aniket Bera
Dinesh Manocha

We present M3ER, a learning-based method for emotion recognition from multiple input modalities. Our approach combines cues from multiple co-occurring modalities (such as face, text, and speech) and also is more robust than other methods to sensor noise in any of the individual modalities. M3ER models a novel, data-driven multiplicative fusion method to combine the modalities, which learn to emphasize the more reliable cues and suppress others on a persample basis. By introducing a check step which uses Canonical Correlational Analysis to differentiate between ineffective and effective modalities, M3ER is robust to sensor noise. M3ER also generates proxy features in place of the ineffectual modalities. We demonstrate the efﬁciency of our network through experimentation on two benchmark datasets, IEMOCAP and CMU-MOSEI. We report a mean accuracy of 82. 7% on IEMOCAP and 89. 0% on CMU-MOSEI, which, collectively, is an improvement of about 5% over prior work.

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AAAI Conference 2020 Conference Paper

STEP: Spatial Temporal Graph Convolutional Networks for Emotion Perception from Gaits

Uttaran Bhattacharya
Trisha Mittal
Rohan Chandra
Tanmay Randhavane
Aniket Bera
Dinesh Manocha

We present a novel classiﬁer network called STEP, to classify perceived human emotion from gaits, based on a Spatial Temporal Graph Convolutional Network (ST-GCN) architecture. Given an RGB video of an individual walking, our formulation implicitly exploits the gait features to classify the perceived emotion of the human into one of four emotions: happy, sad, angry, or neutral. We train STEP on annotated real-world gait videos, augmented with annotated synthetic gaits generated using a novel generative network called STEP-Gen, built on an ST-GCN based Conditional Variational Autoencoder (CVAE). We incorporate a novel pushpull regularization loss in the CVAE formulation of STEP- Gen to generate realistic gaits and improve the classiﬁcation accuracy of STEP. We also release a novel dataset (E-Gait), which consists of 4, 227 human gaits annotated with perceived emotions along with thousands of synthetic gaits. In practice, STEP can learn the affective features and exhibits classiﬁcation accuracy of 88% on E-Gait, which is 14–30% more accurate over prior methods.

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AAAI Conference 2018 Conference Paper

Game of Sketches: Deep Recurrent Models of Pictionary-Style Word Guessing

Ravi Kiran Sarvadevabhatla
Shiv Surya
Trisha Mittal
R. Venkatesh Babu

The ability of machine-based agents to play games in humanlike fashion is considered a benchmark of progress in AI. In this paper, we introduce the ﬁrst computational model aimed at Pictionary, the popular word-guessing social game. We ﬁrst introduce Sketch-QA, an elementary version of Visual Question Answering task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Notably, Sketch-QA involves asking a ﬁxed question (“What object is being drawn? ”) and gathering open-ended guess-words from human guessers. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.

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