# Track Everything: Limiting Prior Knowledge in Online Multi-Object Recognition

## TL;DR

This paper proposes a novel online multi-object tracking and classification system that minimizes prior knowledge, using a biologically inspired approach with a shallow CNN, demonstrating competitive results on benchmark data.

## Contribution

It introduces a general-purpose, robust tracking and classification framework that transfers prior knowledge from detection to classification, reducing reliance on object-specific features.

## Key findings

- Competitive performance on Neovision2 Tower benchmark
- Effective object recognition without prior object-specific knowledge
- Biologically inspired adaptive learning of shape and motion

## Abstract

This paper addresses the problem of online tracking and classification of multiple objects in an image sequence. Our proposed solution is to first track all objects in the scene without relying on object-specific prior knowledge, which in other systems can take the form of hand-crafted features or user-based track initialization. We then classify the tracked objects with a fast-learning image classifier that is based on a shallow convolutional neural network architecture and demonstrate that object recognition improves when this is combined with object state information from the tracking algorithm. We argue that by transferring the use of prior knowledge from the detection and tracking stages to the classification stage we can design a robust, general purpose object recognition system with the ability to detect and track a variety of object types. We describe our biologically inspired implementation, which adaptively learns the shape and motion of tracked objects, and apply it to the Neovision2 Tower benchmark data set, which contains multiple object types. An experimental evaluation demonstrates that our approach is competitive with state-of-the-art video object recognition systems that do make use of object-specific prior knowledge in detection and tracking, while providing additional practical advantages by virtue of its generality.