Sanjit K. Kaul

Sanchit Kaul, Kevin Nhu, Jason Eissayou et al.

This empirical investigation elucidates the limitations of deterministic, unidimensional productivity heuristics by operationalizing the SPACE framework through extensive repository mining. Utilizing a dataset derived from open-source repositories, the study employs rigorous statistical methodologies including Generalized Linear Mixed Models (GLMM) and RoBERTa-based sentiment classification to synthesize a holistic, multi-faceted productivity metric. Analytical results reveal a statistically significant positive correlation between negative affective states and commit frequency, implying a cycle of iterative remediation driven by frustration. Furthermore, the investigation has demonstrated that analyzing the topology of contributor interactions yields superior fidelity in mapping collaborative dynamics compared to traditional volume-based metrics. Ultimately, this research posits a Composite Productivity Score (CPS) to address the heterogeneity of developer efficacy.

Sanchit Kaul, Joseph Luna, Shray Arora

Lifting Structure-from-Motion (SfM) information from sequential and non-sequential image data is a time-consuming and computationally expensive task. In addition to this, the majority of publicly available data is unfit for processing due to inadequate camera pose variation, obscuring scene elements, and noisy data. To solve this problem, we introduce PoolNet, a versatile deep learning framework for frame-level and scene-level validation of in-the-wild data. We demonstrate that our model successfully differentiates SfM ready scenes from those unfit for processing while significantly undercutting the amount of time state of the art algorithms take to obtain structure-from-motion data.

Shivangi Agarwal, Sanjit K. Kaul, Saket Anand et al.

We propose a novel deep neural network (DNN) based approximation architecture to learn estimates of measurements. We detail an algorithm that enables training of the DNN. The DNN estimator only uses measurements, if and when they are received over a communication network. The measurements are communicated over a network as packets, at a rate unknown to the estimator. Packets may suffer drops and need retransmission. They may suffer waiting delays as they traverse a network path. Works on estimation often assume knowledge of the dynamic model of the measured system, which may not be available in practice. The DNN estimator doesn't assume knowledge of the dynamic system model or the communication network. It doesn't require a history of measurements, often used by other works. The DNN estimator results in significantly smaller average estimation error than the commonly used Time-varying Kalman Filter and the Unscented Kalman Filter, in simulations of linear and nonlinear dynamic systems. The DNN need not be trained separately for different communications network settings. It is robust to errors in estimation of network delays that occur due to imperfect time synchronization between the measurement source and the estimator. Last but not the least, our simulations shed light on the rate of updates that result in low estimation error.

Anil Sharma, Saket Anand, Sanjit K. Kaul

Surveillance camera networks are a useful infrastructure for various visual analytics applications, where high-level inferences and predictions could be made based on target tracking across the network. Most multi-camera tracking works focus on target re-identification and trajectory association problems to track the target. However, since camera networks can generate enormous amount of video data, inefficient schemes for making re-identification or trajectory association queries can incur prohibitively large computational requirements. In this paper, we address the problem of intelligent scheduling of re-identification queries in a multi-camera tracking setting. To this end, we formulate the target tracking problem in a camera network as an MDP and learn a reinforcement learning based policy that selects a camera for making a re-identification query. The proposed approach to camera selection does not assume the knowledge of the camera network topology but the resulting policy implicitly learns it. We have also shown that such a policy can be learnt directly from data. Using the NLPR MCT and the Duke MTMC multi-camera multi-target tracking benchmarks, we empirically show that the proposed approach substantially reduces the number of frames queried.

Anil Sharma, Prabhat Kumar, Saket Anand et al.

Target tracking in a camera network is an important task for surveillance and scene understanding. The task is challenging due to disjoint views and illumination variation in different cameras. In this direction, many graph-based methods were proposed using appearance-based features. However, the appearance information fades with high illumination variation in the different camera FOVs. We, in this paper, use spatial and temporal information as the state of the target to learn a policy that predicts the next camera given the current state. The policy is trained using Q-learning and it does not assume any information about the topology of the camera network. We will show that the policy learns the camera network topology. We demonstrate the performance of the proposed method on the NLPR MCT dataset.

Mayank K. Pal, Rupali Bhati, Anil Sharma et al.

Our premise is that autonomous vehicles must optimize communications and motion planning jointly. Specifically, a vehicle must adapt its motion plan staying cognizant of communications rate related constraints and adapt the use of communications while being cognizant of motion planning related restrictions that may be imposed by the on-road environment. To this end, we formulate a reinforcement learning problem wherein an autonomous vehicle jointly chooses (a) a motion planning action that executes on-road and (b) a communications action of querying sensed information from the infrastructure. The goal is to optimize the driving utility of the autonomous vehicle. We apply the Q-learning algorithm to make the vehicle learn the optimal policy, which makes the optimal choice of planning and communications actions at any given time. We demonstrate the ability of the optimal policy to smartly adapt communications and planning actions, while achieving large driving utilities, using simulations.