Private Explainable Video Proccessing

Prexable is a user-friendly, web-based video analysis platform that lets anyone train and understand their own AI models privately and securely—no expertise required. Unlike typical black-box solutions, Prexable ensures transparency, privacy, and full user control over data.

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WhatWeDo?

Prexable is the first explainable private video processing product

with the following unique features:

Explainable AI

Ways to help users understand and trust how AI makes decisions.

Privacy

No need to upload your confidential videos to our servers.

Drag and drop

The simple, drag and drop Prexable interface makes it perfect for non-expert users. 

Cost effective

Cloud-based public and semi-private modes make Prexable completely scalable and cost effective. 

All-In-OneProduct

Prexable provides one single interface for the three mostly used video processing application:

Detection

Tracking

Recognition

OUR FEATURES

OnePlatform.FourCoreCapabilities.

A unified AI architecture that transforms visual data, complex information and real-world signals into intelligent, actionable outcomes.

AI COREIntelligence Engine
COMPUTER VISION

DETECTION & TRACKING

Automatically detect and track objects across video streams for real-time analysis and continuous scene monitoring.

Object DetectionObject TrackingReal-Time Monitoring
IDENTITY INTELLIGENCE

AUTHENTICATION & RECOGNITION

Identify subjects and verify identities using intelligent facial and object recognition across video frames.

Face RecognitionObject RecognitionIdentity Verification
DATA ANNOTATION

VIDEO ANNOTATION

Label, tag, and manage visual data with intuitive annotation tools designed for training, auditing, and human review.

Video LabelingObject TaggingHuman-in-the-Loop
EXPLAINABLE AI

AI REVIEW & EXPLANATION

Understand and validate AI decisions with explainable insights and review tools that improve transparency and trust.

Explainable AIResult ValidationDecision Transparency

LatestBlogs

Vision-Language Models for Video Analytics: Uses & Limits
8 min read October 21, 2026

Vision-Language Models for Video Analytics: Uses & Limits

Discover how vision-language models are transforming video analytics through natural-language search, real-world applications, and AI-powered video understanding. Explore key benefits, limitations, and practical implementation strategies.

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What is HOTA? Higher Order Tracking Accuracy Metric Explained
6 min read September 20, 2026

What is HOTA? Higher Order Tracking Accuracy Metric Explained

HOTA (Higher Order Tracking Accuracy) is a metric for evaluating multi-object tracking (MOT) algorithms. It decomposes tracking errors into detection accuracy (DetA), association accuracy (AssA), and localization accuracy (LocA), then combines DetA and AssA via geometric mean averaged over localization thresholds. HOTA is preferred over MOTA and IDF1 because it provides a balanced evaluation of detection, association, and localization. This guide explains the HOTA formula, how DetA and AssA are computed, why geometric mean is used, and how HOTA compares to MOTA and IDF1.

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From Tracking Objects to Understanding Events: The Rise of Temporal Video AI
5 min read September 3, 2026

From Tracking Objects to Understanding Events: The Rise of Temporal Video AI

Temporal Video AI is transforming video analytics from object detection and tracking toward deeper understanding of actions, events, interactions, and temporal relationships. By analyzing sequences rather than isolated frames, modern AI systems can identify meaningful events, locate when they occur, reason over long videos, and connect visual information with natural language. This evolution enables more capable applications in surveillance, industrial monitoring, sports, robotics, and autonomous systems while introducing new challenges in computational efficiency, long-range temporal reasoning, consistency, and explainability.

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Visual Prompting: The Next Interface for Computer Vision
4 min read August 3, 2026

Visual Prompting: The Next Interface for Computer Vision

Visual prompting is transforming computer vision by replacing task-specific models with prompt-driven foundation models. Instead of training a new AI model for every application, users can guide a single model using points, bounding boxes, sketches, masks, or natural language prompts. This approach enables flexible tasks such as object detection, segmentation, localization, and visual reasoning while reducing development time and annotation costs. Powered by foundation models and Visual Language Models (VLMs), visual prompting is already improving workflows in healthcare, agriculture, robotics, and industrial inspection. Although challenges such as prompt quality, computational cost, and domain-specific accuracy remain, visual prompting is rapidly emerging as the primary interface between humans and AI-powered vision systems.

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From Detection to Understanding
3 min read April 11, 2026

From Detection to Understanding

Segment Anything Model 2 (SAM 2) represents a shift in video AI from multi‑stage pipelines (detection, tracking, annotation) toward unified foundation models. Instead of separate systems for each task, SAM 2 can segment, track, and assist annotation in both images and videos using simple prompts such as clicks or bounding boxes. Its streaming memory architecture enables temporal consistency across frames, improving robustness to motion, occlusion, and appearance changes. This significantly accelerates dataset annotation, reduces labeling cost, and improves consistency. Beyond efficiency, segmentation-based tracking also improves explainability by showing pixel‑level regions influencing predictions. While challenges remain—such as reliance on prompts and difficulty in visually complex scenes—SAM‑like models signal a new paradigm in video AI where systems interact with visual data rather than just process it.

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RF-DETR: The Underrated Contender Redefining Object Detection
3 min read January 27, 2026

RF-DETR: The Underrated Contender Redefining Object Detection

RF-DETR (Refined Focal DETR) is a next-generation object detection architecture that improves upon traditional DETR models by addressing their core limitations. By leveraging Refined Focal Attention and modern backbones like DINOv2, RF-DETR delivers high localization accuracy, reduced false positives, and efficient inference suitable for edge deployment. It maintains DETR’s elegant end-to-end pipeline while offering a practical balance between precision, performance, and deployability, making it a strong alternative to YOLO and Grounding DINO for trained detection tasks.

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When AI Sees Wrong: Common Pitfalls & Biases in Video Analytics, and How to Catch Them Early
5 min read November 27, 2025

When AI Sees Wrong: Common Pitfalls & Biases in Video Analytics, and How to Catch Them Early

This article explores the most common failure modes in modern video analytics—fragile visual inputs, hidden biases, annotation inconsistencies, over-trusted pretrained models, and the limitations of traditional evaluation metrics. It explains how explainability techniques such as concept-based analysis, saliency maps, and counterfactuals can reveal why models fail and how to catch these issues early to build more reliable, trustworthy AI video systems.

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