Deepak Kumar Poddar

High Performance Front Camera ADAS Applications on TI's TDA3X Platform

Advanced driver assistance systems (ADAS) are designed to increase drivers situational awareness and road safety by providing essential information, warning and automatic intervention to reduce the possibility/severity of an accident. Of the various types of ADAS modalities available, camera based ADAS are being widely adopted for their usefulness in varied applications, overall reliability and adaptability to new requirements. But camera based ADAS also represents a complex, high-performance, and low-power compute problem, requiring specialized solutions. This paper introduces a high performance front camera ADAS based on a small area, low power System-on-Chip (SoC) solution from Texas Instruments called Texas Instruments Driver Assist 3x (TDA3x). The paper illustrates compute capabilities of the device in implementation of a typical front camera ADAS. The paper also introduces key programming concepts related to heterogeneous programmable compute cores in the SoC and the software framework to use those cores in order to develop the front camera solutions. These aspects will be of interest not only to the ADAS developers but for computer vision and compute intensive embedded system development.

A Diverse Low Cost High Performance Platform for Advanced Driver Assistance System (ADAS) Applications

Advanced driver assistance systems (ADAS) are becoming more and more popular. Lot of the ADAS applications such as Lane departure warning (LDW), Forward Collision Warning (FCW), Automatic Cruise Control (ACC), Auto Emergency Braking (AEB), Surround View (SV) that were present only in high-end cars in the past have trickled down to the low and mid end vehicles. Lot of these applications are also mandated by safety authorities such as EUNCAP and NHTSA. In order to make these applications affordable in the low and mid end vehicles, it is important to have a cost effective, yet high performance and low power solution. Texas Instruments (TIs) TDA3x is an ideal platform which addresses these needs. This paper illustrates mapping of different algorithms such as SV, LDW, Object detection (OD), Structure From Motion (SFM) and Camera-Monitor Systems (CMS) to the TDA3x device, thereby demonstrating its compute capabilities. We also share the performance for these embedded vision applications, showing that TDA3x is an excellent high performance device for ADAS applications.

Efficient object detection and classification on low power embedded systems

Identifying real world 3D objects such as pedestrians, vehicles and traffic signs using 2D images is a challenging task. There are multiple approaches to tackle this problem with varying degree of detection accuracy and implementation complexity. Some approaches use “hand coded” object features such as Histogram of Oriented Gradients (HOG), Haar, Scale Invariant Feature Transform (SIFT) along with a linear classifier such as Support Vector Machine (SVM), Adaptive Boosting (AdaBoost) to detect objects. Recent developments have shown that a deep multi-layered Convolution Neural Network (CNN) classifier can learn the object features on its own and also classify at an accuracy surpassing human vision. In this paper we combine both the approaches; “object detection” is done using HOG features and AdaBoost cascade classifier and “object classification” is done using CNN to classify the type of objects being detected. The proposed method is implemented on TIs low power TDA3x SoC.

Multichannel video software solution for an asynchronous multiprocessor system

Multichannel video usecases put considerable resource demands on the processing system. Software designed for single channel when extended to multichannel often does not exploit the capabilities of the system fully and limits the number of channels that can be processed simultaneously. In this paper, we discuss the performance constraints observed in an asynchronous multiprocessor system when extending a single channel codec software solution to a multichannel usecase. This is followed by a discussion on building a multichannel codec solution using the existing single channel framework by exploiting bunch submission and parallelizing the operations of different processing cores to maximize the system resource utilization. Upto 30% channel density gains are observed when migrating from the single channel to multichannel solution.

Real time Structure from Motion for Driver Assistance System

Understanding of 3D surrounding is an important problem in Advanced Driver Assistance Systems (ADAS). Structure from Motion (SfM) is well known computer vision technique for estimating 3D structure from 2D image sequences. Inherent complexities of the SfM pose different algorithmic and implementation challenges to have an efficient enablement on embedded processor for real time processing. This paper focuses on highlighting such challenges and innovative solutions for them. The paper proposes an efficient SfM solution that has been implemented on Texas Instruments TDA3x series of System on Chip (SoC). The TDA3x SoC contains one vector processor (known as EVE) and two C66x DSPs as co-processors which are useful for computationally intensive vision processing. The proposed SfM solution which performs Sparse Optical Flow, Fundamental matrix estimation, Triangulation, 3D points pruning consumes 42% of EVE and 10% of one DSP for 25 fps processing of one mega pixel image resolution.