No setor automotivo, os sistemas de auxílio ao condutor são desenvolvidos para reduzir os efeitos colaterais do nível de mobilidade atingido atualmente, como os acidentes de trânsito e os congestionamentos. Em uma perspectiva futura, pretende-se atingir o nível de direção autônoma e cooperativa baseada em redes de sensores. A estratégia de detecção e rastreamento de faixas rodoviárias proposta neste trabalho se enquadra nos requisitos funcionais de alguns desses sistemas, como o Lane Departure Warning (Aviso de Saída de Faixa) e o Lane Keep Assist (Assistência de Manutenção de Faixa). O desenvolvimento do algoritmo foi organizado em três níveis de processamento; baixo, médio e alto. Na etapa de processamento de baixo nível realizam-se as operações de preparação e melhoramento da imagem de entrada, na etapa de nível médio realiza-se a extração das características de interesse e a etapa de alto nível consiste da técnica de rastreamento da posição das faixas. Avaliou-se a resposta do algoritmo, para as amostras escolhidas, por meio de métricas baseadas no distanciamento das faixas rastreadas em relação à posição original das mesmas. Constatou-se que a estratégia apresenta boa precisão nos cenários considerados ideias, inclusive com a presença de sombras, curvas, aclive e declive na estrada. Contudo, essa precisão é comprometida quando a faixa é segmentada, mal sinalizada e quando o reflexo na pista ou o ofuscamento afeta a captura da imagem pela câmera. / In the automotive field, driver assistance systems are developed to reduce the collateral effects of the actual level of mobility, such as traffic jams and accidents. In a future perspective, it is intended to achieve the level of autonomous and cooperative driving based on sensor networks. The proposed strategy for road lanes detection and tracking fits as a functional requirement for some of these systems, as the Lane Departure Warning and Lane Keep Assist. The algorithm development was structured based in three processing levels: low, mid and high levels. The low-level processing enhances the input image, the mid-level processing is an interest feature extractor, and the high-level is the lane position tracking strategy. The algorithm's response evaluation, for the chosen samples, was realized with metrics based on the deviation between the tracked and the original lane. The strategy shows good accuracy levels at the ideal scenario, including shadows, curves, and road slope. However, the accuracy is impaired if the lane is dashed, badly signalized and if road reflection or dazzle harm the image capture by the camera.

No setor automotivo, os sistemas de auxílio ao condutor são desenvolvidos para reduzir os efeitos colaterais do nível de mobilidade atingido atualmente, como os acidentes de trânsito e os congestionamentos. Em uma perspectiva futura, pretende-se atingir o nível de direção autônoma e cooperativa baseada em redes de sensores. A estratégia de detecção e rastreamento de faixas rodoviárias proposta neste trabalho se enquadra nos requisitos funcionais de alguns desses sistemas, como o Lane Departure Warning (Aviso de Saída de Faixa) e o Lane Keep Assist (Assistência de Manutenção de Faixa). O desenvolvimento do algoritmo foi organizado em três níveis de processamento; baixo, médio e alto. Na etapa de processamento de baixo nível realizam-se as operações de preparação e melhoramento da imagem de entrada, na etapa de nível médio realiza-se a extração das características de interesse e a etapa de alto nível consiste da técnica de rastreamento da posição das faixas. Avaliou-se a resposta do algoritmo, para as amostras escolhidas, por meio de métricas baseadas no distanciamento das faixas rastreadas em relação à posição original das mesmas. Constatou-se que a estratégia apresenta boa precisão nos cenários considerados ideias, inclusive com a presença de sombras, curvas, aclive e declive na estrada. Contudo, essa precisão é comprometida quando a faixa é segmentada, mal sinalizada e quando o reflexo na pista ou o ofuscamento afeta a captura da imagem pela câmera. / In the automotive field, driver assistance systems are developed to reduce the collateral effects of the actual level of mobility, such as traffic jams and accidents. In a future perspective, it is intended to achieve the level of autonomous and cooperative driving based on sensor networks. The proposed strategy for road lanes detection and tracking fits as a functional requirement for some of these systems, as the Lane Departure Warning and Lane Keep Assist. The algorithm development was structured based in three processing levels: low, mid and high levels. The low-level processing enhances the input image, the mid-level processing is an interest feature extractor, and the high-level is the lane position tracking strategy. The algorithm's response evaluation, for the chosen samples, was realized with metrics based on the deviation between the tracked and the original lane. The strategy shows good accuracy levels at the ideal scenario, including shadows, curves, and road slope. However, the accuracy is impaired if the lane is dashed, badly signalized and if road reflection or dazzle harm the image capture by the camera.

The research attempts to evaluate the maintaining of the chain of evidence as a process of effective collection, handling and preservation of evidence. The concept "chain of evidence" refers to the process of collecting, handling and preservation of evidence until its presentation in court, as part of the investigation process. Evidence is anything that tends logically to prove or disprove a fact at issue in a judicial case. Evidence essentially consists of oral evidence, documentary evidence and real evidence. The value of evidence cannot be underestimated as evidence can make or break a case. It is therefore important that evidence is correctly and properly collected, handled and preserved to establish a strong link between an individual and a specific act. / Police Practice / Thesis ((M. Tech. (Forensic Investigation) Police Practice))

The research attempts to evaluate the maintaining of the chain of evidence as a process of effective collection, handling and preservation of evidence. The concept "chain of evidence" refers to the process of collecting, handling and preservation of evidence until its presentation in court, as part of the investigation process. Evidence is anything that tends logically to prove or disprove a fact at issue in a judicial case. Evidence essentially consists of oral evidence, documentary evidence and real evidence. The value of evidence cannot be underestimated as evidence can make or break a case. It is therefore important that evidence is correctly and properly collected, handled and preserved to establish a strong link between an individual and a specific act. / Police Practice / Thesis ((M. Tech. (Forensic Investigation) Police Practice))

Indiana University-Purdue University Indianapolis (IUPUI) / Pedestrian and vehicle interaction analysis in a naturalistic driving environment can provide useful information for designing vehicle-pedestrian crash warning/mitigation systems. Many researchers have used crash data to understand and study pedestrian behaviors and interactions between vehicles and pedestrian during crash. However, crash data may not provide detailed pedestrian-vehicle interaction information for us. In this thesis, we designed an automatic pedestrian tracking and status analysis method to process and study pedestrian and vehicle interactions. The proposed pedestrian tracking and status analysis method includes pedestrian detection, pedestrian tracking and pedestrian status analysis modules. The main contributions of this thesis are: we designed a new pedestrian tracking method by learning the pedestrian appearance and also their motion pattern. We designed a pedestrian status estimation method by using our tracking results and thus helped estimate the possibility of collision. Our preliminary experiment results using naturalistic driving data showed promising results.