Abhishek S.

Key Skills

Software

OpenCV AI Kit (OAK)

Other

AWS SageMaker

Top Subject Matter

Computer vision for intelligent traffic control (vehicle detection and density estimation).

Recommendation systems using text metadata feature extraction.

Top Data Types

Text

Image

Video

Top Task Types

Object Detection

Classification

Data Collection

Freelancer Overview

Data Analyst — Major Project: Density-Based Traffic Signal System (object detection annotation and labeling).. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Engineering, Advanced College of Engineering and Management (2025). AI-training focus includes data types such as Video, Computer Code, and Programming and labeling workflows including Object Detection, Data Collection, and Computer Programming.

Labeling Experience

Academic Researcher (ongoing) — structured data work via C/C++/Python and MySQL.

Other

Worked as an academic researcher using C, C++, and Python while demonstrating ability to follow complex structured guidelines. Built and queried large datasets in MySQL, supporting structured data organization for recurring data-intensive tasks. Completed repetitive programming assignments with sustained accuracy and attention to detail. • Followed structured guidelines for producing consistent outputs. • Organized and managed datasets using MySQL. • Produced accurate results across repetitive data-intensive programming tasks.

2021 - Present

Data Analyst — Major Project: Density-Based Traffic Signal System (object detection annotation and labeling).

OtherVideoObject Detection

Applied YOLO v5 and YOLO v7 object detection to annotate and classify real-time vehicle density data across multiple traffic scenarios. Maintained labeling accuracy by drawing and validating bounding boxes and comparing model outputs for consistency. Created structured labeling guidelines to standardize class tagging and improve quality on large video datasets. • Labeled bounding boxes for detected vehicle-density entities. • Ensured consistent class labeling across hundreds of video frames. • Flagged edge cases and ambiguous detections to improve annotation reliability. • Handled complex visual conditions such as multi-lane frames, occlusions, and varying lighting.

2024 - 2025

Density-Based Traffic Signal System (Major Project) — Data Analyst (Aug 2024–Mar 2025)

OpenCV AI Kit (OAK)Object Detection

Engineered an AI-driven traffic signal controller using computer vision to identify vehicles in real time. Compared YOLOv5 and YOLOv7 models to optimize efficiency, speed, and detection precision for live vehicle identification. Used live vehicle density data to drive an intelligent light control algorithm that dynamically adjusts signal durations based on detected objects. • Implemented and evaluated YOLOv5/YOLOv7 for real-time vehicle identification. • Designed a dynamic duration adjustment algorithm from detected vehicle density. • Conducted model comparison to improve detection performance metrics. • Integrated computer-vision outputs into a control decision workflow.

2024 - 2025

Team Lead (Movie Recommendation System) - Advanced College of Engineering and Management

VideoClassification

In this project leadership role, you guided the data collection and preprocessing pipeline to produce structured, accurately labeled training datasets. You applied content-based filtering to model recommendations, requiring precise classification and tagging of movie metadata attributes. You also managed team workflows to ensure consistent application of annotation guidelines. • Lead data collection and preprocessing for training datasets • Apply content-based filtering and tag movie metadata • Coordinate team task assignments and review outputs • Enforce annotation consistency and guideline adherence

2023 - 2024

Team Lead — Minor Project: Movie Recommendation System (labeled training dataset creation).

OtherData Collection

Led data collection and preprocessing to produce structured, accurately labeled training datasets for a movie recommendation system. Applied content-based filtering techniques that required precise tagging of movie metadata attributes. Coordinated team assignments to enforce annotation consistency and adherence to labeling guidelines. • Collected and preprocessed dataset entries for model training. • Labeled/tagged movie metadata attributes used for content-based filtering. • Ensured consistent dataset labeling across the team.

2023 - 2024

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Education

Work History