Leveraging Long-Term Predictions and Online Learning in Agent-Based Multiple Person Tracking

We present a multiple-person tracking algorithm, based on combining particle fi lters and RVO, an agent-based crowd model that infers collision-free velocities so as to predict pedestrian's motion. In addition to position and velocity, our tracking algorithm can estimate the internal goals (desired destination or desired velocity) of the tracked pedestrian in an online manner, thus removing the need to specify this information beforehand. Furthermore, we leverage the longer-term predictions of RVO by deriving a higher-order particle fllter, which aggregates multiple predictions from different prior time steps. This yields a tracker that can recover from short-term occlusions and spurious noise in the appearance model. Experimental results show that our tracking algorithm is suitable for predicting pedestrians' behaviors online without needing scene priors or hand-annotated goal information, and improves tracking in real-world crowded scenes under low frame rates

Algoritmo rápido de seguimiento de objetos

En el presente trabajo de grado se propone reducir la complejidad computacional de un algoritmo de detección, identificación y seguimiento de objetos en secuencias de video. Para el cumplimiento de este objetivo, se proponen dos métodos: filtrado de partículas y flujo óptico. En este documento, inicialmente se hace una contextualización de la técnica en la que se basa el algoritmo de seguimiento de la Secretaría de Movilidad de Bogotá (YOLO) y se hace una descripción detallada de los dos métodos propuestos. Posteriormente, se propone un protocolo de pruebas que permita garantizar el cumplimiento de los objetivos propuestos y se realizan las pruebas pertinentes.The objective of the present work is to reduce the computational complexity of an algorithm for detecting, identifying and tracking objects in video sequences. For the fulfillment of this objective, two methods were proposed: particle filter and optical flow. In this document, a contextualization of the technique on which the tracking algorithm of the Bogota’s Mobility Secretary is based (YOLO), and a detailed description of the two methods proposed is made. Subsequently, a test protocol is proposed to guarantee the fulfillment of the proposed objectives and relevant tests are made.Ingeniero (a) ElectrónicoPregrad